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Stop warning when parent run ID not present
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I_kwDOIPDwls5rYzkw
| 7,610 |
Issue: Big issue with inefficient search from Google/Wikipedia and LLM hallucinations with ReAct agent
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[] | null | 1 | 2023-07-12T20:00:50 | 2023-10-18T16:05:33 | 2023-10-18T16:05:32 |
NONE
| null |
### Issue you'd like to raise.
Hi, LangChain community,
just to share that even with the examples mentioned in the official documentation, it is almost impossible to get consistent results when using agents with Wikipedia/Google search tools. Or the search is non conclusive or the LLM is totally hallucinating at the very early step of the pipeline. LLMs from OpenAI for Completion or Conversation.
Did someone else notice this new behavior?
Best regards
Jerome
### Suggestion:
_No response_
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I_kwDOIPDwls5rYsr3
| 7,609 |
BaseEmbaasLoader validation fails
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[] | null | 3 | 2023-07-12T19:38:41 | 2023-07-13T07:50:31 | 2023-07-13T07:50:30 |
NONE
| null |
### System Info
Repro:
Running this code sample.
https://github.com/techleadhd/chatgpt-retrieval
```
Traceback (most recent call last):
File "/home/maciej/workdir/intenzia/langchaintest/chatgpt-retrieval/chatgpt.py", line 5, in <module>
from langchain.chains import ConversationalRetrievalChain, RetrievalQA
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/agents/agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/agents/tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/tools/__init__.py", line 3, in <module>
from langchain.tools.arxiv.tool import ArxivQueryRun
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/tools/arxiv/tool.py", line 12, in <module>
from langchain.utilities.arxiv import ArxivAPIWrapper
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/utilities/__init__.py", line 3, in <module>
from langchain.utilities.apify import ApifyWrapper
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/utilities/apify.py", line 5, in <module>
from langchain.document_loaders import ApifyDatasetLoader
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/document_loaders/__init__.py", line 44, in <module>
from langchain.document_loaders.embaas import EmbaasBlobLoader, EmbaasLoader
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/langchain/document_loaders/embaas.py", line 54, in <module>
class BaseEmbaasLoader(BaseModel):
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/main.py", line 204, in __new__
fields[ann_name] = ModelField.infer(
^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 488, in infer
return cls(
^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 419, in __init__
self.prepare()
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 539, in prepare
self.populate_validators()
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 801, in populate_validators
*(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/validators.py", line 696, in find_validators
yield make_typeddict_validator(type_, config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/validators.py", line 585, in make_typeddict_validator
TypedDictModel = create_model_from_typeddict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/annotated_types.py", line 35, in create_model_from_typeddict
return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/main.py", line 972, in create_model
return type(__model_name, __base__, namespace)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/main.py", line 204, in __new__
fields[ann_name] = ModelField.infer(
^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 488, in infer
return cls(
^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 419, in __init__
self.prepare()
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 534, in prepare
self._type_analysis()
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/site-packages/pydantic/fields.py", line 638, in _type_analysis
elif issubclass(origin, Tuple): # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/maciej/.pyenv/versions/3.11.0/lib/python3.11/typing.py", line 1550, in __subclasscheck__
return issubclass(cls, self.__origin__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: issubclass() arg 1 must be a class
```
```
langchain==0.0.230
langchainplus-sdk==0.0.20
Python 3.11.0
Ubuntu 22.04
```
```
maciej@ola:~/workdir/intenzia/langchaintest/chatgpt-retrieval$ pip freeze
aiohttp==3.8.4
aiosignal==1.3.1
anyio==3.7.1
async-timeout==4.0.2
attrs==23.1.0
backoff==2.2.1
certifi==2023.5.7
cffi==1.15.1
chardet==5.1.0
charset-normalizer==3.2.0
chromadb==0.3.27
click==8.1.4
clickhouse-connect==0.6.6
coloredlogs==15.0.1
cryptography==41.0.2
dataclasses-json==0.5.9
duckdb==0.8.1
et-xmlfile==1.1.0
fastapi==0.85.1
filetype==1.2.0
flatbuffers==23.5.26
frozenlist==1.3.3
greenlet==2.0.2
h11==0.14.0
hnswlib==0.7.0
httptools==0.6.0
humanfriendly==10.0
idna==3.4
importlib-metadata==6.8.0
joblib==1.3.1
langchain==0.0.230
langchainplus-sdk==0.0.20
lxml==4.9.3
lz4==4.3.2
Markdown==3.4.3
marshmallow==3.19.0
marshmallow-enum==1.5.1
monotonic==1.6
mpmath==1.3.0
msg-parser==1.2.0
multidict==6.0.4
mypy-extensions==1.0.0
nltk==3.8.1
numexpr==2.8.4
numpy==1.25.1
olefile==0.46
onnxruntime==1.15.1
openai==0.27.8
openapi-schema-pydantic==1.2.4
openpyxl==3.1.2
overrides==7.3.1
packaging==23.1
pandas==2.0.3
pdf2image==1.16.3
pdfminer.six==20221105
Pillow==10.0.0
posthog==3.0.1
protobuf==4.23.4
pulsar-client==3.2.0
pycparser==2.21
pydantic==1.9.0
pypandoc==1.11
python-dateutil==2.8.2
python-docx==0.8.11
python-dotenv==1.0.0
python-magic==0.4.27
python-pptx==0.6.21
pytz==2023.3
PyYAML==6.0
regex==2023.6.3
requests==2.31.0
six==1.16.0
sniffio==1.3.0
SQLAlchemy==2.0.18
starlette==0.20.4
sympy==1.12
tabulate==0.9.0
tenacity==8.2.2
tiktoken==0.4.0
tokenizers==0.13.3
tqdm==4.65.0
typing-inspect==0.9.0
typing_extensions==4.7.1
tzdata==2023.3
unstructured==0.8.1
urllib3==2.0.3
uvicorn==0.22.0
uvloop==0.17.0
watchfiles==0.19.0
websockets==11.0.3
xlrd==2.0.1
XlsxWriter==3.1.2
yarl==1.9.2
zipp==3.16.0
zstandard==0.21.0
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run this code sample
https://github.com/techleadhd/chatgpt-retrieval
### Expected behavior
Pydantic validation fails: TypeError: issubclass() arg 1 must be a class
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PR_kwDOIPDwls5VV_qE
| 7,608 |
make client explicit in runner
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[] | null | 1 | 2023-07-12T18:59:01 | 2023-07-13T07:32:05 | 2023-07-13T07:32:03 |
COLLABORATOR
| null |
More explicit / a req from harrison
|
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| 1,801,561,607 |
PR_kwDOIPDwls5VV7A-
| 7,607 |
Fix Lint: Bittensor llm
|
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[] | null | 5 | 2023-07-12T18:43:05 | 2023-07-15T22:37:27 | 2023-07-15T22:37:15 |
CONTRIBUTOR
| null |
I fixed the lint so it passes checks for pull #7602
I didn't remove the bearer key in the headers on line 58 in case it's there on purpose for whatever reason that could be. However, @Kunj-2206 if that's not left in on purpose, either reply here to remove it or git checkout this pull req and make a new one.
|
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| 1,801,539,064 |
I_kwDOIPDwls5rYU34
| 7,606 |
If we continuously send "Hello" messages to the "conversational retrieval chain," the model produces weired/wrong answers.
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[] | null | 9 | 2023-07-12T18:25:59 | 2024-01-05T13:06:23 | 2023-10-18T16:05:37 |
NONE
| null |
### System Info
langchain==0.0.170
openai==0.27.4
python
windows
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am using the "**ConversationalRetrievalChain**", Whenever I send a message to a model, it produces wrong results with history, whereas if I send the same message without history, it produces correct results. The issue here is that the model is forming the wrong standalone question, and the same wrong standalone question is passing to the OpenAI Model.
Steps to reproduce:
1. Use the Conversational Retrieval Chan
2. Pass the history on to the chain.
3. Send the "Hello" message continuously.
Here you can observe wrong/weired answers.
The above scenario is Just an example. But whenever we ask some question, immediately if we send the message "Hello", the "conversational retrieval chain" forms the wrong standalone question, hence the openAI model producing wrong answers.
This is the code I am using: -
_template = """
Use the following pieces of context to answer the question at the end.
{context}
If you still cant find the answer, just say that you don't know, don't try to make up an answer.
You can also look into chat history.
{chat_history}
Question: {question}
Answer:
"""
CONDENSE_QUESTION_PROMPT = PromptTemplate(
template=_template,
input_variables = ["context","question", "chat_history"],
)
chain = ConversationalRetrievalChain.from_llm(
llm= azure_chat_api_llm_objct,
retriever=vectors.as_retriever(),
verbose=True,
chain_type="stuff",
memory = memory,
get_chat_history=lambda h:h,
# condense_question_prompt=CONDENSE_QUESTION_PROMPT,
combine_docs_chain_kwargs={"prompt": CONDENSE_QUESTION_PROMPT},
return_generated_question=True,
)
chain.run("Hello")
Note:- I have tried multiple way like removing "CONDENCE_QUESTION", without prompt, with different types of prompts, etc..but still its producing wrong standalone question.
Ex:-
_template = """
Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:
"""
Thanks in advance, and please correct me if I made any mistakes in the code.
<img width="458" alt="image" src="https://github.com/hwchase17/langchain/assets/52491904/eb0e3aba-6151-42f8-924b-bda2cb4ccfaf">
### Expected behavior
It should form a proper standalone question when we pass history before passing to model.
|
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| 1,801,502,750 |
I_kwDOIPDwls5rYMAe
| 7,605 |
Type Error Langchain auto converting string to date.timestamp in Pinecone similarity search when Document pagecontent value is a string that "could" be a date ex: 21070926
|
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[] | null | 2 | 2023-07-12T18:01:54 | 2023-10-18T16:05:43 | 2023-10-18T16:05:42 |
NONE
| null |
### System Info
langchain==0.0.206 ; python_version >= "3.11" and python_version < "4.0"
langchainplus-sdk==0.0.16 ; python_version >= "3.11" and python_version < "4.0"
### Who can help?
@hwaking @agola11 Hey Guys! The pinecone wrapper is doing a weird auto-type conversion and its thinking my string ID values are dates in this part of the code:
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Have a string value set as your Pinecone Document.pagecontent that could be misinterpreted as a date ex: 21070809
Problem function:
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[dict] = None,
namespace: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""Return pinecone documents most similar to query, along with scores.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Dictionary of argument(s) to filter on metadata
namespace: Namespace to search in. Default will search in '' namespace.
Returns:
List of Documents most similar to the query and score for each
"""
if namespace is None:
namespace = self._namespace
query_obj = self._embedding_function(query)
docs = []
results = self._index.query(
[query_obj],
top_k=k,
include_metadata=True,
namespace=namespace,
filter=filter,
)
for res in results["matches"]:
metadata = res["metadata"]
if self._text_key in metadata:
text = metadata.pop(self._text_key)
score = res["score"]
# if (type(text) != str): <-------------------------- I added this code to convert it back to string
# text = text.strftime("%Y%m%d") <------------- If you can just recast to string the problem will resolve
docs.append((Document(page_content=text, metadata=metadata), score))
else:
logger.warning(
f"Found document with no `{self._text_key}` key. Skipping."
)
return docs
Stack: [langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/uvicorn/protocols/http/h11_impl.py", line 428, in run_asgi
[langchain] [2023-07-12 17:43:08] result = await app( # type: ignore[func-returns-value]
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 78, in __call__
[langchain] [2023-07-12 17:43:08] return await self.app(scope, receive, send)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/fastapi/applications.py", line 282, in __call__
[langchain] [2023-07-12 17:43:08] await super().__call__(scope, receive, send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 348, in _sentry_patched_asgi_app
[langchain] [2023-07-12 17:43:08] return await middleware(scope, receive, send)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/asgi.py", line 141, in _run_asgi3
[langchain] [2023-07-12 17:43:08] return await self._run_app(scope, lambda: self.app(scope, receive, send))
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/asgi.py", line 190, in _run_app
[langchain] [2023-07-12 17:43:08] raise exc from None
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/asgi.py", line 185, in _run_app
[langchain] [2023-07-12 17:43:08] return await callback()
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/applications.py", line 122, in __call__
[langchain] [2023-07-12 17:43:08] await self.middleware_stack(scope, receive, send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 143, in _create_span_call
[langchain] [2023-07-12 17:43:08] return await old_call(app, scope, new_receive, new_send, **kwargs)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/middleware/errors.py", line 184, in __call__
[langchain] [2023-07-12 17:43:08] raise exc
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/middleware/errors.py", line 162, in __call__
[langchain] [2023-07-12 17:43:08] await self.app(scope, receive, _send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 242, in _sentry_exceptionmiddleware_call
[langchain] [2023-07-12 17:43:08] await old_call(self, scope, receive, send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 143, in _create_span_call
[langchain] [2023-07-12 17:43:08] return await old_call(app, scope, new_receive, new_send, **kwargs)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 79, in __call__
[langchain] [2023-07-12 17:43:08] raise exc
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
[langchain] [2023-07-12 17:43:08] await self.app(scope, receive, sender)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/starlette.py", line 143, in _create_span_call
[langchain] [2023-07-12 17:43:08] return await old_call(app, scope, new_receive, new_send, **kwargs)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/fastapi/middleware/asyncexitstack.py", line 20, in __call__
[langchain] [2023-07-12 17:43:08] raise e
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/fastapi/middleware/asyncexitstack.py", line 17, in __call__
[langchain] [2023-07-12 17:43:08] await self.app(scope, receive, send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/routing.py", line 718, in __call__
[langchain] [2023-07-12 17:43:08] await route.handle(scope, receive, send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/routing.py", line 276, in handle
[langchain] [2023-07-12 17:43:08] await self.app(scope, receive, send)
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/starlette/routing.py", line 66, in app
[langchain] [2023-07-12 17:43:08] response = await func(request)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/sentry_sdk/integrations/fastapi.py", line 131, in _sentry_app
[langchain] [2023-07-12 17:43:08] return await old_app(*args, **kwargs)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/fastapi/routing.py", line 241, in app
[langchain] [2023-07-12 17:43:08] raw_response = await run_endpoint_function(
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/fastapi/routing.py", line 167, in run_endpoint_function
[langchain] [2023-07-12 17:43:08] return await dependant.call(**values)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/search.py", line 528, in ask_question_0_gateway
[langchain] [2023-07-12 17:43:08] return await ask_question_0(question, user)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/search.py", line 282, in ask_question_0
[langchain] [2023-07-12 17:43:08] response = await pinecone_search(question, metadata_filter)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/search.py", line 274, in pinecone_search
[langchain] [2023-07-12 17:43:08] return pine.similarity_search_with_score(query=question, k=k, filter=filter)
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/langchain/vectorstores/pinecone.py", line 132, in similarity_search_with_score
[langchain] [2023-07-12 17:43:08] docs.append((Document(page_content=text, metadata=metadata), score))
[langchain] [2023-07-12 17:43:08] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[langchain] [2023-07-12 17:43:08] File "/workspace/.heroku/python/lib/python3.11/site-packages/langchain/load/serializable.py", line 64, in __init__
[langchain] [2023-07-12 17:43:08] super().__init__(**kwargs)
[langchain] [2023-07-12 17:43:08] File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
[langchain] [2023-07-12 17:43:08] raise validation_error
[langchain] [2023-07-12 17:43:08] pydantic.error_wrappers.ValidationError: 1 validation error for Document
[langchain] [2023-07-12 17:43:08] page_content
[langchain] [2023-07-12 17:43:08] str type expected (type=type_error.str)
### Expected behavior
I would expect my data from Pinecone to comeback to my API exactly how I put it there. Remove the auto-conversion or implement a strict string type casting to ensure no auto-typing.
|
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| 7,604 |
Share client
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[] | null | 1 | 2023-07-12T17:38:16 | 2023-07-12T21:04:57 | 2023-07-12T21:04:56 |
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| 1,801,241,754 |
I_kwDOIPDwls5rXMSa
| 7,603 |
Add support for Meilisearch vector databases
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[] | null | 3 | 2023-07-12T15:32:23 | 2023-07-29T00:06:55 | 2023-07-29T00:06:55 |
CONTRIBUTOR
| null |
### Feature request
Add support for Meilisearch vector search.
[Meilisearch](https://www.meilisearch.com) is an open-source search engine. See [documentation](https://www.meilisearch.com/docs)
### Motivation
Meilisearch is releasing the vector search/store feature, which should be available from July 31st.
### Your contribution
I'm working on it and will submit a PR for this issue soon.
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PR_kwDOIPDwls5VUv2B
| 7,602 |
add bittensor llm to langchain llms
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[] | null | 1 | 2023-07-12T15:21:48 | 2023-07-13T12:28:50 | 2023-07-13T12:28:50 |
CONTRIBUTOR
| null |
- Description: Adding BittensorLLM via Validator Endpoint to langchain llms
- Tag maintainer: @Kunj-2206
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
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PR_kwDOIPDwls5VUbjU
| 7,601 |
Create langchain/llms/bittensor.py
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[] | null | 1 | 2023-07-12T14:40:54 | 2023-07-12T14:42:13 | 2023-07-12T14:42:08 |
CONTRIBUTOR
| null |
Added BittensorLLM model inside langchain.llms
<!-- 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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,801,022,338 |
I_kwDOIPDwls5rWWuC
| 7,599 |
ConversationalRetrievalChain with streaming=True => responds by repeating words and phrases
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[] | null | 9 | 2023-07-12T13:46:03 | 2023-11-23T02:34:29 | null |
NONE
| null |
### System Info
Python: 3.10.11
LangChain: 0.0.229
SO: Windows, Linux Ubuntu and Mac
Hi people,
I'm using **ConversationalRetrievalChain** without any modifications, and in 90% of the cases, it responds by repeating words and entire phrases, like in the examples below:
Questions about Duna book:
**How did Muad'Dib die?**
Muad'Dib died of old age. **No, Muad'Dib did not die of old age**. The passage mentions that the death of Muad'Dib will be followed by the moral suicide of all men, suggesting that his death was not natural. However, the exact cause of his death is not specified in the text. No, Muad'Dib did not die of old age. The passage mentions that the death of Muad'Dib will be followed by the moral suicide of all men, suggesting that his death was not natural. **However, the exact cause of his death is not specified in the text**.
**Was Paul Atreides powerful?**
**Was Paul Atreides powerful? Yes, Paul Atreides was powerful**. He possessed oracle-like abilities and special powers that placed him beyond the usual limits of time and space. Additionally, he was a charismatic leader and skilled strategist, capable of commanding armies and achieving victories. His strength and power were recognized by the emperor and other characters in the book. Yes, Paul Atreides was powerful. **He possessed oracle-like abilities and special powers that placed him beyond the usual limits of time and space. Additionally, he was a charismatic leader and skilled strategist, capable of commanding armies and achieving victories. His strength and power were recognized by the emperor and other characters in the book.**
**PLEASE, Does anyone know about this issue?**
I have repeated several tutorials that I found on the internet, and this always happens. But strangely, I don't see anyone complaining about it in the forums, so I must be doing something wrong.
**This is the code I am using:**
llm = ChatOpenAI(model_name=app_params.Model,
temperature=0,
openai_api_key=app_keys.OPENAI_API_KEY,
**streaming=True,**
verbose=True,
callbacks=[WebSocketCallbackHandler(websocket)]
)
memory = ConversationBufferWindowMemory(memory_key='chat_history', output_key='answer', chat_memory=chat_history, return_messages=True, k=3, verbose=True)
qna = ConversationalRetrievalChain.from_llm(
llm=llm,
chain_type="stuff",
retriever=retriever,
verbose=True,
memory=memory,
)
#async
result = **await qna.acall**({"question": query})
**I would be very glad for any help!
Best regards,
Marcos.**
Hi, @hwchase17, I appreciate your help.
### Who can help?
Hi, @hwchase17, I appreciate your help.
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**This is the code I am using:**
llm = ChatOpenAI(model_name=app_params.Model,
temperature=0,
openai_api_key=app_keys.OPENAI_API_KEY,
**streaming=True,**
verbose=True,
callbacks=[WebSocketCallbackHandler(websocket)]
)
memory = ConversationBufferWindowMemory(memory_key='chat_history', output_key='answer', chat_memory=chat_history, return_messages=True, k=3, verbose=True)
qna = ConversationalRetrievalChain.from_llm(
llm=llm,
chain_type="stuff",
retriever=retriever,
verbose=True,
memory=memory,
)
**#async**
result = **await qna.acall**({"question": query})
### Expected behavior
I hope the answer provided by ConversationalRetrievalChain makes sense and does not contain repetitions of the question or entire phrases.
|
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I_kwDOIPDwls5rWP61
| 7,598 |
Output parser set number of list result
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[] | null | 3 | 2023-07-12T13:31:51 | 2023-10-19T16:06:03 | 2023-10-19T16:06:02 |
NONE
| null |
### Issue you'd like to raise.
I'd like to understand if there is a way to have from OpenAI response a **list of values** (string or objects: it's the same) of a **fixed length**. I read the documentation here: https://python.langchain.com/docs/modules/model_io/output_parsers/comma_separated but I didn't find anything related to a length (maybe it's not possible?). Indeed I understood that probably these parsers only add a well-formatted piece of prompt, but you don't have the safety to have the expected results.
In my use case, I have a list of texts and I want to have a title on each of them. My prompt is something like that:
```
####
Text 1:
- Bla bla bla
...
Text 8:
- Bla bla bla
###
Use these texts to generate a title for each text. The number of the titles must be 8.
```
At the end of the prompt I "force" the number to the number of the expected results with the same number of the text, but sometimes the model gives me more titles (above all when the number of texts is 1).
So I thought to use these parsers. But I don't find any constraints on the length of the results, the only thing is a validator to check if the length is correct.
### Suggestion:
_No response_
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| 7,597 |
Question!! Multiple agent use? agent within agent?
|
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[] | null | 6 | 2023-07-12T13:10:59 | 2023-12-13T03:46:39 | null |
NONE
| null |
### Feature request
Hello Langchain community!
I'm currently in the process of developing a company's chatbot, and I've chosen to use both a CSV file and Pinecone DB for the project.
Here's a basic outline of the structure I've adopted so far:
I've managed to set the two tools, and its example usage has been providing accurate answers
the first tool gets me the answers based on pandas’s result from the example usage, the answers are based on csv and it’s correct in all cases
Also set the second tool and its example usage is answered correctly.
until here things are very promising and i expected everything to work as it is.
so i have set the LLM and combined the two tools and used agent
However, when I combined both tools using an agent, the answers started to deviate from the expected output. I'm not entirely sure whether the method I'm using to utilize the agent is optimal.
To address this issue, I've experimented with the MultiretrievalQA chain using vector embedding. But the results are not consistently reliable, and moreover, I'd rather not generate new embeddings every time I modify the CSV.
Is there anyone in the community who can shed light on these issues I'm encountering? Any feedback on my current approach, suggestions on how to optimize it, or alternative strategies would be greatly appreciated!
Thank you.
### Motivation
I'm making a company's gpt and i hope to link my csv with the chatbot so that whenever i change the csv the chatbot is automatically linked with it
### Your contribution
um solving the problem would help others?
|
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PR_kwDOIPDwls5VTefX
| 7,596 |
fix logging debug error with textloader when autodetect_encoding=True
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[] | null | 1 | 2023-07-12T12:12:13 | 2023-08-01T01:17:02 | 2023-08-01T01:17:02 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: fix logging debug error with textloader when autodetect_encoding=True
- Issue: -
- Dependencies:
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @Buckler89
|
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PR_kwDOIPDwls5VTaZx
| 7,595 |
Removed kwargs from add_texts
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[] | null | 3 | 2023-07-12T12:02:43 | 2023-07-19T16:23:54 | 2023-07-19T16:23:49 |
CONTRIBUTOR
| null |
Removing **kwargs argument from add_texts method in DeepLake vectorstore as it confuses users and doesn't fail when user is typing incorrect parameters.
Also added small test to ensure the change is applies correctly.
Guys could pls take a look: @rlancemartin, @eyurtsev, this is a small PR.
Thx so much!
<!-- 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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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I_kwDOIPDwls5rVcUF
| 7,594 |
Error while trying to run SQL Database Agent example
|
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[] | null | 2 | 2023-07-12T11:33:16 | 2023-10-11T11:11:24 | null |
CONTRIBUTOR
| null |
### System Info
Langchain version: 0.0.230
python version: Python 3.9.12
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Im trying run the to code mentioned at: https://python.langchain.com/docs/modules/agents/toolkits/sql_database
But I'm getting the error as:
ModuleNotFoundError: No module named 'MySQLdb'
Then trying 'pip install MySQL-python' gives the following error:
ModuleNotFoundError: No module named 'ConfigParser'
Then trying 'pip install configparser' even doesn't solve the issue.
Please help me figure out this issue. Thanks!
### Expected behavior
The code should have just executed the prompt, And installation of the required libraries should be easier.
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PR_kwDOIPDwls5VTAjM
| 7,593 |
fix(typo): Clarify the point of `llm_chain`
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[] | null | 2 | 2023-07-12T10:54:20 | 2023-07-12T14:31:00 | 2023-07-12T14:31:00 |
CONTRIBUTOR
| null |
Fixes a typo introduced in https://github.com/hwchase17/langchain/pull/7080 by @hwchase17.
In the example (visible on [the online documentation](https://api.python.langchain.com/en/latest/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html#langchain-chains-conversational-retrieval-base-conversationalretrievalchain)), the `llm_chain` variable is unused as opposed to being used for the question generator. This change makes it clearer.
|
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PR_kwDOIPDwls5VS6b0
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Update bedrock.py - support of other endpoint url (esp. for users of …
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[] | null | 4 | 2023-07-12T10:37:09 | 2023-07-19T12:39:10 | 2023-07-12T14:43:24 |
CONTRIBUTOR
| null |
Added an _endpoint_url_ attribute to Bedrock(LLM) class - I have access to Bedrock only via us-west-2 endpoint and needed to change the endpoint url, this could be useful to other users
<!-- 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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
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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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PR_kwDOIPDwls5VSloH
| 7,591 |
Add local support for audio models (PR #7329)
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[] | null | 3 | 2023-07-12T09:44:45 | 2023-08-02T08:24:54 | 2023-08-02T08:24:53 |
CONTRIBUTOR
| null |
- Description: run the poetry dependencies
- Issue: #7329
- Dependencies: any dependencies required for this change,
- Tag maintainer: @rlancemartin
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I_kwDOIPDwls5rUrXJ
| 7,590 |
Improve the usage of relevance score threshold and allow the return of the scores
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[] | null | 2 | 2023-07-12T09:38:17 | 2023-10-18T16:05:52 | 2023-10-18T16:05:52 |
NONE
| null |
### Feature request
In `VectorStore`/`VectorStoreRetriever` class, `_similarity_search_with_relevance_scores`function,
1) Allow different choices of threshold kind: allow users to choose whether >= threshold or <= threshold
2) Allow users to choose to return the relevance score along with the docs
### Motivation
I am working with returning relevant docs that satisfy a certain threshold, and I encountered some problems.
One problem is that for different embedding algorithms and similarity calculations, it is not always the case that higher relevance scores are better. Actually, when I use `HuggingFaceEmbedding` with `Chroma` database, the smaller the relevance score, the better. So I believe it would be necessary to allow users choose between different options here.
The second problem is that I want to see the relevance score, however, in https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/base.py, line 474 to 492, it is fixed that only the docs are finally returned.
```
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **self.search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
query, **self.search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
```
I wish there is an option that the relevance scores could also be returned.
### Your contribution
I would love to open a PR if applicable ;) @hwchase17
|
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I_kwDOIPDwls5rUpsS
| 7,589 |
namespace parameter is gone in pinecone from_existing_index method
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[] | null | 2 | 2023-07-12T09:34:19 | 2023-07-12T14:45:09 | 2023-07-12T14:45:09 |
NONE
| null |
### System Info
I am using langchain v0.0.228. But namespace parameter is gone in existing_index() of v0.0.230.
Why the namespace is gone?
@hwchase17 @eyurtsev @agola11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
No need.
### Expected behavior
should be used from_existing_index method with namespace parameter
|
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| 7,588 |
Question：ConversationalRetrievalChain with retriever
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[] | null | 1 | 2023-07-12T09:28:48 | 2023-07-13T03:37:20 | 2023-07-13T03:37:20 |
NONE
| null |
### Issue you'd like to raise.
if use ElasticVectorSearch as as_retriever and set the params:
```python
elastic_vector_search.as_retriever(search_type="similarity_score_threshold",
search_kwargs={"score_threshold": 1.8})
```
If no relevant docs were retrieved using the relevance score threshold 1.8.
then return an error!
### Suggestion:
it should not throw err, when there is no relevant docs.
it should response like "i don't know " or an openai default response.
|
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| 7,587 |
docs : Use more meaningful examples
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[] | null | 2 | 2023-07-12T09:19:24 | 2023-07-12T14:31:56 | 2023-07-12T14:31:56 |
CONTRIBUTOR
| null |
This change makes the ecosystem integrations cnosdb documentation more realistic and easy to understand.
- change examples of question and table
- modify typo and format
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[] | null | 2 | 2023-07-12T08:21:47 | 2023-10-27T16:06:54 | 2023-10-27T16:06:53 |
NONE
| null |
### System Info
MacOS
Python 3.10.6
langchain 0.0.230
langchainplus-sdk 0.0.20
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
For now, there are only the default `ConversationalChatAgent` and `ConversationalAgent`, and we cannot create custom prompt templates in them. At least, I don't see how to do that in both the docs and source code.
I think there should be a straight-forward way to do so, just like how you can have a [custom LLM agent](https://python.langchain.com/docs/modules/agents/how_to/custom_llm_chat_agent):
```py
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
```
However, when the same code is applied to `ConversationalChatAgent`, that is:
```py
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["\nObservation:"],
allowed_tools=tool_names
)
```
I get the following error:
```shell
pydantic.error_wrappers.ValidationError: 1 validation error for ConversationalChatAgent
__root__
Got unexpected prompt type <class '__main__.CustomPromptTemplate'> (type=value_error)
```
I'm not sure if this belongs to **Bug Report** so if I'm doing anything wrong, please tell me about it. Thanks!
### Expected behavior
The agent should be created without errors.
|
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PR_kwDOIPDwls5VR89H
| 7,584 |
bump 231
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[] | null | 1 | 2023-07-12T07:58:17 | 2023-07-12T14:43:13 | 2023-07-12T14:43:12 |
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PR_kwDOIPDwls5VRnat
| 7,583 |
Fix PGVector
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[] | null | 2 | 2023-07-12T06:52:09 | 2023-07-12T06:55:14 | 2023-07-12T06:55:14 |
CONTRIBUTOR
| null |
- Description: Fixes parameter fills for PGVector
- Issue:
https://github.com/hwchase17/langchain/issues/7577
- Dependencies: any dependencies required for this change,
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PR_kwDOIPDwls5VRlyh
| 7,582 |
unit test sqlalachemy
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I_kwDOIPDwls5rTKxc
| 7,581 |
DOC: Langchain works well with single database, but in a session if I have to work with multiple database, it does not seem working
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[] | null | 7 | 2023-07-12T05:02:43 | 2023-12-21T00:49:57 | 2023-07-12T05:06:10 |
NONE
| null |
### Issue with current documentation:
Below code does not seem to work,
```
db = SQLDatabase.from_uri("mssql+pymssql://<some server>/<some db>",
include_tables=['Some table'], view_support=True)
db1 = SQLDatabase.from_uri("mssql+pymssql://<some other server>/<some other db>",
include_tables=['Some other table'], view_support=True)
toolkit = SQLDatabaseToolkit(db=db, llm=llm, reduce_k_below_max_tokens=True)
sql_agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True
)
toolkit1 = SQLDatabaseToolkit(db=db1, llm=llm, reduce_k_below_max_tokens=True)
sql_agent_executor1 = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True
)
tools = [
Tool(
name = "Object or Product to Classification Association",
func=sql_agent_executor.run,
description="""
Useful for when you need to Query on database to find the object or product to classification association.
<user>: Get me top 3 records with Object number and description for approved classification KKK
<assistant>: I need to check Object or Product to Classification Association details.
<assistant>: Action: SQL Object or Product to Classification Association
<assistant>: Action Input: Check The Object or Product to Classification Association Table
"""
),
Tool(
name = "Authorization or Authority or License Database",
func=sql_agent_executor1.run,
description="""
Useful for when you need to Query on some thing else .
<user>: Get me top 2 Authority Records with LicenseNumber
<assistant>: I need to check Authorization or Authority or License Database details.
<assistant>: Action: SQL Authorization or Authority or License Database
<assistant>: Action Input: Check The Authorization or Authority or License Database Table
"""
)
]
```
Is there an example where we can configure multiple databases as different tools and query them. It seems possible, but in my case, what ever question I ask, it always goes to the first Tool. Not sure what is the problem.
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5VRM5A
| 7,580 |
Harrison/pg vector move
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| 1,800,138,889 |
I_kwDOIPDwls5rS_CJ
| 7,579 |
sqlalchemy fails to initialize with KeyError "EmbeddingStore"
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[] | null | 1 | 2023-07-12T04:18:26 | 2023-07-12T07:35:27 | 2023-07-12T07:35:27 |
CONTRIBUTOR
| null |
### Why do I care about this issue?
MLflow also uses sqlalchemy to handle sqlite-based storage, and calls `sqlalchemy.orm.configure_mappers()` during initialization. With langchain>=0.0.228, MLflow fails to start. All MLflow users who choose sqlite-based storage cannot use it with langchain.
### System Info
langchain>=0.0.228
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Executing the following command fails with langchain >= 0.0.228, and no error occurs for langchain <= 0.0.227:
```python
import langchain
import sqlalchemy
sqlalchemy.orm.configure_mappers()
```
Error:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/clsregistry.py:515, in _class_resolver._resolve_name(self)
514 if rval is None:
--> 515 rval = d[token]
516 else:
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/util/_collections.py:346, in PopulateDict.__missing__(self, key)
345 def __missing__(self, key: Any) -> Any:
--> 346 self[key] = val = self.creator(key)
347 return val
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/clsregistry.py:483, in _class_resolver._access_cls(self, key)
481 return value
--> 483 return self.fallback[key]
KeyError: 'EmbeddingStore'
The above exception was the direct cause of the following exception:
InvalidRequestError Traceback (most recent call last)
Cell In[3], line 1
----> 1 sqlalchemy.orm.configure_mappers()
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/mapper.py:4167, in configure_mappers()
4099 def configure_mappers():
4100 """Initialize the inter-mapper relationships of all mappers that
4101 have been constructed thus far across all :class:`_orm.registry`
4102 collections.
(...)
4164
4165 """
-> 4167 _configure_registries(_all_registries(), cascade=True)
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/mapper.py:4198, in _configure_registries(registries, cascade)
4192 Mapper.dispatch._for_class(Mapper).before_configured() # type: ignore # noqa: E501
4193 # initialize properties on all mappers
4194 # note that _mapper_registry is unordered, which
4195 # may randomly conceal/reveal issues related to
4196 # the order of mapper compilation
-> 4198 _do_configure_registries(registries, cascade)
4199 finally:
4200 _already_compiling = False
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/mapper.py:4239, in _do_configure_registries(registries, cascade)
4237 if not mapper.configured:
4238 try:
-> 4239 mapper._post_configure_properties()
4240 mapper._expire_memoizations()
4241 mapper.dispatch.mapper_configured(mapper, mapper.class_)
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/mapper.py:2403, in Mapper._post_configure_properties(self)
2400 self._log("initialize prop %s", key)
2402 if prop.parent is self and not prop._configure_started:
-> 2403 prop.init()
2405 if prop._configure_finished:
2406 prop.post_instrument_class(self)
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/interfaces.py:578, in MapperProperty.init(self)
571 """Called after all mappers are created to assemble
572 relationships between mappers and perform other post-mapper-creation
573 initialization steps.
574
575
576 """
577 self._configure_started = True
--> 578 self.do_init()
579 self._configure_finished = True
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/relationships.py:1632, in RelationshipProperty.do_init(self)
1630 self._check_conflicts()
1631 self._process_dependent_arguments()
-> 1632 self._setup_entity()
1633 self._setup_registry_dependencies()
1634 self._setup_join_conditions()
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/relationships.py:1849, in RelationshipProperty._setup_entity(self, _RelationshipProperty__argument)
1842 resolved_argument: _ExternalEntityType[Any]
1844 if isinstance(argument, str):
1845 # we might want to cleanup clsregistry API to make this
1846 # more straightforward
1847 resolved_argument = cast(
1848 "_ExternalEntityType[Any]",
-> 1849 self._clsregistry_resolve_name(argument)(),
1850 )
1851 elif callable(argument) and not isinstance(
1852 argument, (type, mapperlib.Mapper)
1853 ):
1854 resolved_argument = argument()
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/clsregistry.py:519, in _class_resolver._resolve_name(self)
517 rval = getattr(rval, token)
518 except KeyError as err:
--> 519 self._raise_for_name(name, err)
520 except NameError as n:
521 self._raise_for_name(n.args[0], n)
File ~/miniconda3/envs/mlflow-dev-env/lib/python3.8/site-packages/sqlalchemy/orm/clsregistry.py:500, in _class_resolver._raise_for_name(self, name, err)
490 raise exc.InvalidRequestError(
491 f"When initializing mapper {self.prop.parent}, "
492 f'expression "relationship({self.arg!r})" seems to be '
(...)
497 f"['{clsarg}']] = relationship()\""
498 ) from err
499 else:
--> 500 raise exc.InvalidRequestError(
501 "When initializing mapper %s, expression %r failed to "
502 "locate a name (%r). If this is a class name, consider "
503 "adding this relationship() to the %r class after "
504 "both dependent classes have been defined."
505 % (self.prop.parent, self.arg, name, self.cls)
506 ) from err
InvalidRequestError: When initializing mapper Mapper[CollectionStore(langchain_pg_collection)], expression 'EmbeddingStore' failed to locate a name ('EmbeddingStore'). If this is a class name, consider adding this relationship() to the <class 'langchain.vectorstores.pgvector.CollectionStore'> class after both dependent classes have been defined.
```
It's likely to be introduced by this PR https://github.com/hwchase17/langchain/pull/7370.
### Expected behavior
No error should occur.
|
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| 1,800,131,260 |
I_kwDOIPDwls5rS9K8
| 7,578 |
chain.run doesn't necessarily return a `str`
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[] | null | 16 | 2023-07-12T04:11:51 | 2023-10-13T02:31:07 | 2023-10-13T02:30:41 |
NONE
| null |
https://github.com/hwchase17/langchain/blob/2667ddc6867421842fe027f1946644f452de8eb3/langchain/chains/base.py#L386-L393
when I have this:
```
chain = create_structured_output_chain(Categorization, llm, prompt, verbose=True)
response = chain.run(trx_description)
```
my `response` object is a dict not a str, but I got misled by the type assistance making me think it was a str.
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I_kwDOIPDwls5rSt-4
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PGVector.from_documents breaking from 0.0.229 to 0.0.230
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[] | null | 2 | 2023-07-12T02:50:01 | 2023-07-13T03:26:29 | 2023-07-13T03:26:29 |
NONE
| null |
### System Info
langchain==0.0.230
### Who can help?
@raymond-yuan
### 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
Fill in appropriate values for parameters
```
db = PGVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
connection_string=CONNECTION_STRING,
pre_delete_collection=True,
)
```
Produces:
```
Traceback (most recent call last):
File "create_pgvector_index_hr.py", line 70, in <module>
db = PGVector.from_documents(
File "/home/coder/venv-openai-slackbot/lib/python3.8/site-packages/langchain/vectorstores/pgvector.py", line 578, in from_documents
return cls.from_texts(
File "/home/coder/venv-openai-slackbot/lib/python3.8/site-packages/langchain/vectorstores/pgvector.py", line 453, in from_texts
return cls.__from(
File "/home/coder/venv-openai-slackbot/lib/python3.8/site-packages/langchain/vectorstores/pgvector.py", line 213, in __from
store = cls(
TypeError: ABCMeta object got multiple values for keyword argument 'connection_string'
```
and appears related to this change: https://github.com/hwchase17/langchain/blame/master/langchain/vectorstores/pgvector.py#L213-L220
### Expected behavior
Above code works with langchain==0.0.229
Code should not throw an exception as it did prior to 0.0.230
|
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PR_kwDOIPDwls5VQstf
| 7,576 |
added id retrieval from redis search setup within the metadata
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[] | null | 2 | 2023-07-12T02:12:07 | 2023-07-18T17:50:57 | 2023-07-18T17:50:57 |
CONTRIBUTOR
| null |
Replace this comment with:
- Description: The modification is done to retrieve the document ids from the relevant document vector search in redis
- Issue: It helps to point out the exact document for down stream operations later
- Tag maintainer: @rlancemartin, @eyurtsev
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| 1,800,025,152 |
PR_kwDOIPDwls5VQqJ1
| 7,575 |
Reduce the number of LLM parsing error with conversational chats
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[] | null | 3 | 2023-07-12T01:54:52 | 2023-09-19T02:16:31 | null |
NONE
| null |
A lot of times, when the LLM responses a Final Answer with markdown in the answer text (e.g. asking the LLM to write some code), the parser raises an OutputParserException("Could not parse LLM output: ..."), when the answer from the LLM is totally acceptable.
This change should reduce the number of such occurances.
- Description: Reduce the number of LLM parsing error with conversational chats,
- Issue: the issue #1358 ,
- Dependencies: None,
- Tag maintainer: @hinthornw @baskaryan,
For example, this response from gpt-turbo-3.5 for "How to convert a list of dict to a JSON in Python" seems totally acceptable as the final answer, but will raise the OutputParserException, even with `handle_parsing_errors=True`. This PR fixes it.
|
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PR_kwDOIPDwls5VQbQi
| 7,573 |
Fixed bug7445 where a duplicate restuld_id is added to the vectorstore.
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[] | null | 1 | 2023-07-12T00:24:07 | 2023-08-01T01:00:02 | 2023-08-01T01:00:02 |
CONTRIBUTOR
| null |
- Description: updated BabyAGI examples to append the iteration to the result id to fix error storing data to vectorstore.
- Issue: 7445
- Dependencies: no
- Tag maintainer: @eyurtsev
- 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!
This fix worked for me locally. Happy to take some feedback and iterate on a better solution. I was considering appending a uuid instead but didnt want to over complicate the example.
|
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| 1,799,951,224 |
PR_kwDOIPDwls5VQab1
| 7,572 |
Return run info from run evaluator
|
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[] | null | 1 | 2023-07-12T00:20:07 | 2023-07-13T05:06:36 | 2023-07-13T05:06:35 |
COLLABORATOR
| null |
Optionally return run info. This is added to the feedback to link feedback and source run.
Depends on the new loading and config workflow slightly
|
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I_kwDOIPDwls5rSQnW
| 7,571 |
AmazonKendraRetriever "Could not load credentials" error in latest release
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[] | null | 1 | 2023-07-12T00:16:40 | 2023-07-13T03:47:37 | 2023-07-13T03:47:37 |
NONE
| null |
### System Info
LangChain version: 0.0.229
Platform: AWS Lambda execution
Python version: 3.9
I get the following error when creating the AmazonKendraRetriever using LangChain version 0.0.229.
Code to create retriever:
`retriever = AmazonKendraRetriever(index_id=kendra_index)`
Error:
```[ERROR] ValidationError: 1 validation error for AmazonKendraRetriever
__root__
Could not load credentials to authenticate with AWS client. Please check that credentials in the specified profile name are valid. (type=value_error)
Traceback (most recent call last):
File "/var/task/lambda_function.py", line 171, in lambda_handler
retriever = AmazonKendraRetriever(index_id=kendra_index)
File "/opt/python/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__```
When using LangChain version 0.0.219 this error does not occur.
Issue also raised on aws-samples git repo with potential solution: https://github.com/aws-samples/amazon-kendra-langchain-extensions/issues/24
### 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
1. Install latest version of Langchain
2. Follow instructions here: https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/amazon_kendra_retriever
### Expected behavior
Error not thrown when creating AmazonKendraRetriever
|
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PR_kwDOIPDwls5VQX6e
| 7,570 |
add supported properties for notiondb document loader's metadata
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[] | null | 4 | 2023-07-12T00:05:05 | 2023-07-12T07:34:55 | 2023-07-12T07:34:55 |
CONTRIBUTOR
| null |
fix #7569
add following properties for Notion DB document loader's metadata
- `unique_id`
- `status`
- `people`
@rlancemartin, @eyurtsev (Since this is a change related to `DataLoaders`)
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I_kwDOIPDwls5rSN1T
| 7,569 |
Issue: Document loader for Notion DB doesn't supports some properties
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[] | null | 0 | 2023-07-12T00:02:03 | 2023-07-12T07:34:56 | 2023-07-12T07:34:56 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Current version of document loader for Notion DB doesn't supports following properties for metadata
- `unique_id`
- https://www.notion.so/help/unique-id
- `status`
- https://www.notion.so/help/guides/status-property-gives-clarity-on-tasks
- `people`
- useful property when you assign some task to assignees
### Suggestion:
I would like to make a PR to fix this issue if it's okay.
|
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| 1,799,921,555 |
PR_kwDOIPDwls5VQT8-
| 7,568 |
Make recursive loader yield while crawling
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[] | null | 2 | 2023-07-11T23:41:16 | 2023-07-14T04:55:21 | 2023-07-14T04:55:21 |
COLLABORATOR
| null |
Support actual lazy_load since it can take a while to crawl larger directories.
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Accept Chain
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[] | null | 1 | 2023-07-11T23:37:47 | 2023-07-13T07:22:35 | 2023-07-13T07:22:34 |
COLLABORATOR
| null |
By default, the run_on_evaluate method requires chains to be passed in via a constructor. If you're not looking at function types, it's common to try to pass your chain in directly. This PR proposes to
- Accept a raw chain if it lacks memory. It may still be leaky if a child chain has memory (or for other sources of randomness), but that could also be the case if the constructor were incorrectly set up
- Throw a helpful error if not, explaining how to fix and why this is a requirement.
I don't update the public API types since we still want people to be in the habit of passing in the constructor, but this feels like a kinder UX
Delta from the general eval config refactor but not directly related
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PR_kwDOIPDwls5VP9PC
| 7,566 |
Revert pinecone v4 support
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[] | null | 1 | 2023-07-11T22:03:40 | 2023-07-12T00:59:00 | 2023-07-12T00:58:59 |
COLLABORATOR
| null |
Revert 9d13dcd
|
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| 1,799,816,745 |
PR_kwDOIPDwls5VP8m3
| 7,565 |
propagate callbacks through load_summarize_chain
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[] | null | 2 | 2023-07-11T22:01:00 | 2023-08-08T16:04:43 | 2023-08-04T03:12:34 |
CONTRIBUTOR
| null |
This lets you pass callbacks when you create the summarize chain:
```
summarize = load_summarize_chain(llm, chain_type="map_reduce", callbacks=[my_callbacks])
summary = summarize(documents)
```
See #5572 for a similar surgical fix.
tagging @hwchase17 for callbacks work
<!-- 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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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PR_kwDOIPDwls5VP3FC
| 7,564 |
Add into chat agent prompt to not prefix json blob with "json"
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[] | null | 3 | 2023-07-11T21:38:19 | 2023-07-12T16:56:04 | 2023-07-12T16:56:04 |
NONE
| null |
Fix bug where LLM was prefixing action JSON with the word "json" causing agent to not be able parse the action/action_input in the JSON.
Response from ChatOpenAI LLM was:
```
json
{
"action": "Calculator",
"action_input": "25% of 300"
}
```
Bot asked me to submit pull request for this fix to issue I filed: https://github.com/hwchase17/langchain/issues/7554
This was breaking the DeepLeaning/Langchain tutorial on Agents: https://learn.deeplearning.ai/langchain/lesson/7/agents
@hinthornw
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| 1,799,768,998 |
PR_kwDOIPDwls5VPyJk
| 7,563 |
Rm old Run Evaluators
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[] | null | 1 | 2023-07-11T21:19:15 | 2023-07-13T07:38:02 | 2023-07-13T07:38:00 |
COLLABORATOR
| null |
Rm old run evaluator implementations + loaders
|
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PR_kwDOIPDwls5VPpEl
| 7,562 |
add browserless loader
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CONTRIBUTOR
| null |
# Browserless
Added support for Browserless' `/content` endpoint as a document loader.
### About Browserless
Browserless is a cloud service that provides access to headless Chrome browsers via a REST API. It allows developers to automate Chromium in a serverless fashion without having to configure and maintain their own Chrome infrastructure.
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PR_kwDOIPDwls5VPlZ9
| 7,561 |
docs: update unstructured docstrings
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[] | null | 2 | 2023-07-11T20:32:13 | 2023-07-11T21:12:05 | 2023-07-11T21:12:05 |
CONTRIBUTOR
| null |
### Summary
Updates the docstrings in the Unstructured document loaders to display more useful information on the integrations page.
### Reviewers
- @rlancemartin
- @eyurtsev
- @hwchase17
|
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PR_kwDOIPDwls5VPauU
| 7,560 |
Adding LLM wrapper for Kobold AI
|
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[] | null | 10 | 2023-07-11T19:54:16 | 2023-07-12T07:48:12 | 2023-07-12T07:48:12 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: adds two wrappers that lets you use Oobabooga's text-generation-webui api or KoboldAI api in lanchain
- Issue: n/a
- Dependencies: none extra, just what exists in lanchain
- Tag maintainer: @baskaryan
- Twitter handle: @zanzibased
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,
I'm not sure if I followed this but I have been testing these for a while and using them with my discord langchain chatbot
2. an example notebook showing its use.
https://colab.research.google.com/drive/1iAVfRnxHpwI4DAyD7rNGlKar212w24Uc?usp=sharing
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5rRHj3
| 7,559 |
` ValueError: Could not parse output` when using `QAGenerateChain`'s `.apply_and_parse()` method
|
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[] | null | 6 | 2023-07-11T19:44:49 | 2023-10-31T16:06:20 | 2023-10-31T16:06:19 |
NONE
| null |
### System Info
python==3.9.17
langchain==0.0.190
Win 11 64 bit
### Who can help?
@hwchase17 @agol
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
# Instantiate the chain
example_gen_chain = QAGenerateChain.from_llm(ChatOpenAI())
example_gen_chain.apply_and_parse([{'doc': data[2]}])
[Out]:
ValueError Traceback (most recent call last)
Cell In[36], line 1
----> 1 example_gen_chain.apply_and_parse([{'doc': data[2]}])
File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\chains\llm.py:257, in LLMChain.apply_and_parse(self, input_list, callbacks)
255 """Call apply and then parse the results."""
256 result = self.apply(input_list, callbacks=callbacks)
--> 257 return self._parse_result(result)
File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\chains\llm.py:263, in LLMChain._parse_result(self, result)
259 def _parse_result(
260 self, result: List[Dict[str, str]]
261 ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
262 if self.prompt.output_parser is not None:
--> 263 return [
264 self.prompt.output_parser.parse(res[self.output_key]) for res in result
265 ]
266 else:
267 return result
File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\chains\llm.py:264, in <listcomp>(.0)
259 def _parse_result(
260 self, result: List[Dict[str, str]]
261 ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
262 if self.prompt.output_parser is not None:
263 return [
--> 264 self.prompt.output_parser.parse(res[self.output_key]) for res in result
265 ]
266 else:
267 return result
File ~\anaconda3\envs\nlp-openai-langchain\lib\site-packages\langchain\output_parsers\regex.py:28, in RegexParser.parse(self, text)
26 else:
27 if self.default_output_key is None:
---> 28 raise ValueError(f"Could not parse output: {text}")
29 else:
30 return {
31 key: text if key == self.default_output_key else ""
32 for key in self.output_keys
33 }
ValueError: Could not parse output: QUESTION: What is the fabric composition of the Maine Expedition Shirt with PrimaLoft®?
ANSWER: The fabric composition of the Maine Expedition Shirt with PrimaLoft® is 85% premium wool and 15% nylon.
```
### Expected behavior
Returns parsed output.
|
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I_kwDOIPDwls5rRAAI
| 7,558 |
Use `func` as a default `coroutine` method in Tool instantiation
|
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[] | null | 2 | 2023-07-11T19:23:13 | 2023-10-17T16:04:49 | 2023-10-17T16:04:48 |
CONTRIBUTOR
| null |
### Feature request
There are tools with `func` but whose implementation of coroutine would be just same. E.g.,
```python
def adder(x, y):
return x+y
async def aadder(x, y):
return x+y
adder_tool = Tool(func=adder, coroutine=aadder, ...)
```
I have to define `adder` and `aadder` redundantly.
Of course the logic can be abstracted within the two definitions, but I'd prefer just having the same function and reuse the `func` at the async calling. A possible implementation would look like this
at [this line](https://github.com/hwchase17/langchain/blob/master/langchain/tools/base.py#L453C1-L473C65)
```python
async def _arun(
self,
*args: Any,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
**kwargs: Any,
) -> Any:
"""Use the tool asynchronously."""
if self.coroutine:
...
elif self.default_coroutine: # introducing some parameter
return self.run(*args, run_manager=run_manager, **kwargs)
raise NotImplementedError("Tool does not support async")
```
with this the adding `adder` will look like this:
```python
def adder(x, y):
return x+y
adder_tool = Tool(func=adder, default_coroutine=True, ...)
```
### Motivation
Simplify function documentations
### Your contribution
I can make a PR as proposed if it's the right approach.
|
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| 1,799,513,554 |
I_kwDOIPDwls5rQmXS
| 7,557 |
Follow-up Query Recommender Callback
|
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[] | null | 2 | 2023-07-11T18:18:50 | 2023-12-21T16:07:34 | 2023-12-21T16:07:33 |
CONTRIBUTOR
| null |
### Feature request
Introduce a follow-up query recommender callback to enhance user experience and engagement in case of chatbot use case.
The recommender callback will suggest relevant follow-up queries generated by LLM based on the user's conversation history, facilitating smoother interactions.
The proposed flow is as follows:
- Utilize the configured memory to recommend follow-up queries by analyzing the chat history.
- In the absence of configured memory, leverage the current question and answer to suggest follow-up queries.
Usage: It's up to the user how they want to utilize these recommended queries
### Motivation
The inclusion of this feature would greatly benefit various chatbot use cases.
By suggesting follow-up queries, the chatbot can proactively guide the conversation, helping users navigate complex interactions more efficiently. This feature has the potential to enhance user satisfaction and streamline the overall user experience. While the exact extent of its usefulness may vary, it is a valuable addition that can significantly improve the chatbot's capabilities.
### Your contribution
I can work on this, let me know your thoughts @hwchase17
|
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PR_kwDOIPDwls5VOn8i
| 7,556 |
Add notebook example for Lemon AI NLP Workflow Automation
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[] | null | 3 | 2023-07-11T17:30:03 | 2023-07-12T07:22:27 | 2023-07-11T19:15:12 |
CONTRIBUTOR
| null |
- Description: Added notebook to LangChain docs that explains how to use Lemon AI NLP Workflow Automation tool with Langchain
- Issue: not applicable
- Dependencies: not applicable
- Tag maintainer: @agola11
- Twitter handle: felixbrockm
|
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Added support for chat_history
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[] | null | 3 | 2023-07-11T17:28:23 | 2023-07-11T19:27:26 | 2023-07-11T19:27:26 |
CONTRIBUTOR
| null |
#7469
|
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| 1,799,345,871 |
I_kwDOIPDwls5rP9bP
| 7,554 |
Using Agent - ChatOpenAI - response can't be parsed because it starts with 'json' for next action (RC identified and fix proposed)
|
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[] | null | 5 | 2023-07-11T16:48:21 | 2023-10-10T16:15:58 | 2023-10-10T16:15:46 |
NONE
| null |
### System Info
LangChain 0.230
python 3.10
From first example of using calculator from: https://learn.deeplearning.ai/langchain/lesson/7/agents
> Entering new chain...
ACTION: json
{
"action": "Calculator",
"action_input": "25% of 300"
}
The text is what the ChatOutputParser in agents/chat/output_parser.py gets (I prefixed it with ACTION in my print statement).
The word 'json' is now prefixing the JSON blob from the ChatOpenAI LLM which causes the agent to fail. One possible solution that I verified work but not sure if it's the right one is checking if it prefixes with json and just remove that:
if action.startswith("json"):
action = action[4:]
This seems to work and lets both the first two examples in the tutorial work.
### Who can help?
@hwchase17 and @agola11 for LLM/Chat wrappers/Agents.
### 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
Steps to reproduce behavior.
Run source code from @hwchase17 Deeplearning class: https://learn.deeplearning.ai/langchain/lesson/7/agents
The calculator and wikipedia examples don't run.
### Expected behavior
The calculator and wikipedia examples work by parsing the "new?" output from ChatOpenAI in the tutorial.
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| 1,799,331,094 |
PR_kwDOIPDwls5VOUXm
| 7,553 |
Lint Fix: Update chat agents' output parser to extract action by regex
|
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[] | null | 2 | 2023-07-11T16:39:17 | 2023-07-28T05:42:23 | 2023-07-28T05:42:23 |
CONTRIBUTOR
| null |
I formatted the code in order to pass the lint checks for the PR #7511
|
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I_kwDOIPDwls5rP5Yp
| 7,552 |
GPTIndexChatMemory doesn't work as expected with langchain and the agent doesn't use the chat history
|
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[] | null | 1 | 2023-07-11T16:38:22 | 2023-10-17T16:04:55 | 2023-10-17T16:04:54 |
NONE
| null |
### System Info
langchain version: '0.0.230'
llama-index version: '0.7.4'
python: 3.10.11
### Who can help?
@hwchase17
@agola11
@eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**When I'm trying to use GPTIndexChatMemory to embed my conversation and store the whole of it, this feature (memory) doesn't work anymore, and this is my code, also I want to save the memory in a folder with its embeddings and I can't**
```
from llama_index.langchain_helpers.memory_wrapper import GPTIndexChatMemory, GPTIndexMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import AgentType
from llama_index import ServiceContext
from llama_index import GPTListIndex
from langchain.embeddings import OpenAIEmbeddings
llm = ChatOpenAI(temperature=0)
embed_model = LangchainEmbedding(OpenAIEmbeddings())
service_context = ServiceContext.from_defaults(embed_model=embed_model)
index = GPTListIndex([], service_context=service_context)
memory = GPTIndexChatMemory(
index=index,
memory_key="chat_history",
query_kwargs={"response_mode": "compact", "service_context":service_context},
input_key="input",
return_messages=True,
return_source= True
)
agent_executor = initialize_agent(
[], llm, verbose = True,agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION, memory=memory, handle_parsing_errors="Check your output and make sure it conforms!"
)
agent_executor.agent.llm_chain.prompt.template = """Assistant is a large language model trained by OpenAI.
Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.
please use the following format:
AI: [your response here]
Begin!
Previous conversation history:
{chat_history}
New input: {input}
{agent_scratchpad}"""
print(agent_executor.run("my name is zeyad"))
```
this would be the output for the first print statement.
**AI: Hello Zeyad! How can I assist you today?**
`print("Do you know my name?")`
**this would be the output for the first print statement (unexcepted output), one week ago it was working fine without any problems.**
**AI: As an AI language model, I don't have access to personal information unless you provide it to me. Therefore, I don't know your name unless you tell me. Is there anything specific you would like assistance with?**
### Expected behavior
the expected output for the second statement must be:
AI: Yes, you told me before that your name is Zeyad.
**I really appreciate any help you can provide.**
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| 1,799,323,572 |
I_kwDOIPDwls5rP3-0
| 7,551 |
Pass a JSON string and get an error mismatched or missing input parameter
|
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[] | null | 3 | 2023-07-11T16:34:25 | 2023-07-12T00:44:36 | 2023-07-12T00:44:36 |
NONE
| null |
### System Info
LangChain: 0.0.230
Python: 3.10
### Who can help?
_No response_
### 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
I have a dictionary in python
```python
dict = {
"keyfeatures": [
{
"title": "Search for Charitable Activities",
"description": "The system must provide a search function that allows users to enter keywords and returns a list of charitable activities that match those keywords."
},
{
"title": "Display Charitable Activities",
"description": "The system must display the search results in a user-friendly format."
},
{
"title": "Filter and Sort Charitable Activities",
"description": "The system must provide options for users to filter and sort the search results."
},
{
"title": "View Details of a Charitable Activity",
"description": "The system must allow users to select a charitable activity from the search results and view more detailed information about it."
},
{
"title": "Save or Bookmark Charitable Activities",
"description": "The system must allow users to save or bookmark charitable activities that they're interested in."
}
]
}
```
i convert the dictionary in a json string
```python
json_string = json.dumps(dict)
```
and i obtain the following string
```json
{"keyfeatures": [{"title": "Search for Charitable Activities", "description": "The system must provide a search function that allows users to enter keywords and returns a list of charitable activities that match those keywords."}, {"title": "Display Charitable Activities", "description": "The system must display the search results in a user-friendly format."}, {"title": "Filter and Sort Charitable Activities", "description": "The system must provide options for users to filter and sort the search results."}, {"title": "View Details of a Charitable Activity", "description": "The system must allow users to select a charitable activity from the search results and view more detailed information about it."}, {"title": "Save or Bookmark Charitable Activities", "description": "The system must allow users to save or bookmark charitable activities that they're interested in."}]}
```
if i pass that string to an AIMessagePromptTemplate
```python
AIMessagePromptTemplate.from_template(msg)
```
i get the following error:
```text
File "/home/andrea/PycharmProjects/ArchAI/venv/lib/python3.10/site-packages/langchain/prompts/chat.py", line 85, in from_template
prompt = PromptTemplate.from_template(template, template_format=template_format)
File "/home/andrea/PycharmProjects/ArchAI/venv/lib/python3.10/site-packages/langchain/prompts/prompt.py", line 145, in from_template
return cls(
File "/home/andrea/PycharmProjects/ArchAI/venv/lib/python3.10/site-packages/langchain/load/serializable.py", line 74, in __init__
super().__init__(**kwargs)
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for PromptTemplate
__root__
Invalid prompt schema; check for mismatched or missing input parameters. '"title"' (type=value_error)
```
i have already added f-string formater with multiple curly-brackets but still fail
thanks in advance
### Expected behavior
the PromptMessage must import a formatted JSON string correctly without interfere with internal template curly-brackets format
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PR_kwDOIPDwls5VOPvr
| 7,550 |
Add env setup
|
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[] | null | 1 | 2023-07-11T16:25:11 | 2023-07-11T16:48:40 | 2023-07-11T16:48:40 |
COLLABORATOR
| null |
Include setup
|
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| 1,799,294,681 |
PR_kwDOIPDwls5VOMUu
| 7,549 |
Lint Fix: Lost in the middle
|
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[] | null | 1 | 2023-07-11T16:17:21 | 2023-07-15T22:38:15 | 2023-07-15T22:38:15 |
CONTRIBUTOR
| null |
Fixed lint checking for PR #7520
|
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I_kwDOIPDwls5rPnwa
| 7,548 |
TypeError: issubclass() arg 1 must be a class when using langchain in azure
|
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[] | null | 22 | 2023-07-11T15:57:23 | 2023-07-12T22:28:37 | 2023-07-12T06:25:35 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.225 also tested with 0.0.229
I can only reproduce it in Azure, I cant reproduce it locally.
### Who can help?
I have a simple python app with streamlit and langchain, I am deploying this to Azure via CI/CD with the following YAML definition
stages:
- stage: Build
displayName: Build stage
jobs:
- job: BuildJob
pool:
vmImage: $(vmImageName)
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: '$(pythonVersion)'
displayName: 'Use Python $(pythonVersion)'
- script: |
python -m venv antenv
source antenv/bin/activate
python -m pip install --upgrade pip
pip install setup streamlit
pip install --target="./.python_packages/lib/site-packages" -r ./requirements.txt
workingDirectory: $(projectRoot)
displayName: "Install requirements"
- task: ArchiveFiles@2
displayName: 'Archive files'
inputs:
rootFolderOrFile: '$(projectRoot)'
includeRootFolder: false
archiveType: zip
archiveFile: $(Build.ArtifactStagingDirectory)/$(Build.BuildId).zip
replaceExistingArchive: true
- upload: $(Build.ArtifactStagingDirectory)/$(Build.BuildId).zip
displayName: 'Upload package'
artifact: drop
- stage: Deploy
displayName: 'Deploy Web App'
dependsOn: Build
condition: succeeded()
jobs:
- deployment: DeploymentJob
pool:
vmImage: $(vmImageName)
environment: $(environmentName)
strategy:
runOnce:
deploy:
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: '$(pythonVersion)'
displayName: 'Use Python version'
- task: AzureAppServiceSettings@1
displayName: 'Set App Settings'
inputs:
azureSubscription: 'AzureAIPocPrincipal'
appName: 'test'
resourceGroupName: 'AzureAIPoc'
appSettings: |
[
{
"name": "ENABLE_ORYX_BUILD",
"value": 1
},
{
"name": "SCM_DO_BUILD_DURING_DEPLOYMENT",
"value": 1
},
{
"name": "POST_BUILD_COMMAND",
"value": "pip install -r ./requirements.txt"
}
]
- task: AzureWebApp@1
displayName: 'Deploy Azure Web App : {{ webAppName }}'
inputs:
azureSubscription: 'AzureAIPocPrincipal'
appType: 'webAppLinux'
deployToSlotOrASE: true
resourceGroupName: 'AzureAIPoc'
slotName: 'production'
appName: 'test'
package: '$(Pipeline.Workspace)/drop/$(Build.BuildId).zip'
startUpCommand: 'python -m streamlit run app/home.py --server.port 8000 --server.address 0.0.0.0'
My requirements file is:
langchain==0.0.225
streamlit
openai
python-dotenv
pinecone-client
streamlit-chat
chromadb
tiktoken
pymssql
typing-inspect==0.8.0
typing_extensions==4.5.0
However I am getting the following error:
TypeError: issubclass() arg 1 must be a class
Traceback:
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/tmp/8db82251b0e58bc/app/pages/xxv0.2.py", line 6, in <module>
import langchain
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/agents/agent.py", line 26, in <module>
from langchain.chains.base import Chain
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/chains/__init__.py", line 2, in <module>
from langchain.chains.api.base import APIChain
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/chains/api/base.py", line 13, in <module>
from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/chains/api/prompt.py", line 2, in <module>
from langchain.prompts.prompt import PromptTemplate
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/prompts/__init__.py", line 12, in <module>
from langchain.prompts.example_selector import (
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/prompts/example_selector/__init__.py", line 4, in <module>
from langchain.prompts.example_selector.semantic_similarity import (
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/prompts/example_selector/semantic_similarity.py", line 8, in <module>
from langchain.embeddings.base import Embeddings
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/embeddings/__init__.py", line 29, in <module>
from langchain.embeddings.sagemaker_endpoint import SagemakerEndpointEmbeddings
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/embeddings/sagemaker_endpoint.py", line 7, in <module>
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/llms/__init__.py", line 52, in <module>
from langchain.llms.vertexai import VertexAI
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/llms/vertexai.py", line 14, in <module>
from langchain.utilities.vertexai import (
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/utilities/__init__.py", line 3, in <module>
from langchain.utilities.apify import ApifyWrapper
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/utilities/apify.py", line 5, in <module>
from langchain.document_loaders import ApifyDatasetLoader
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/document_loaders/__init__.py", line 43, in <module>
from langchain.document_loaders.embaas import EmbaasBlobLoader, EmbaasLoader
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/langchain/document_loaders/embaas.py", line 54, in <module>
class BaseEmbaasLoader(BaseModel):
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/main.py", line 204, in __new__
fields[ann_name] = ModelField.infer(
^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 488, in infer
return cls(
^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 419, in __init__
self.prepare()
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 539, in prepare
self.populate_validators()
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 801, in populate_validators
*(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/validators.py", line 696, in find_validators
yield make_typeddict_validator(type_, config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/validators.py", line 585, in make_typeddict_validator
TypedDictModel = create_model_from_typeddict(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/annotated_types.py", line 35, in create_model_from_typeddict
return create_model(typeddict_cls.__name__, **kwargs, **field_definitions)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/main.py", line 972, in create_model
return type(__model_name, __base__, namespace)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/main.py", line 204, in __new__
fields[ann_name] = ModelField.infer(
^^^^^^^^^^^^^^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 488, in infer
return cls(
^^^^
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 419, in __init__
self.prepare()
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 534, in prepare
self._type_analysis()
File "/tmp/8db82251b0e58bc/antenv/lib/python3.11/site-packages/pydantic/fields.py", line 638, in _type_analysis
elif issubclass(origin, Tuple): # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/python/3.11.3/lib/python3.11/typing.py", line 1570, in __subclasscheck__
return issubclass(cls, self.__origin__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
I am not copying here the app script as the code works locally, I think its something more related to Azure App Service Plan Environment or the venv setup in the yaml file.
### 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
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
n/a
### Expected behavior
code should work :)
|
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| 1,799,224,657 |
I_kwDOIPDwls5rPf1R
| 7,547 |
0.0.229 VespaRetriver signature broken
|
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[] | null | 3 | 2023-07-11T15:41:42 | 2023-11-01T16:06:35 | 2023-11-01T16:06:34 |
NONE
| null |
### System Info
langchain 0.0.229
cannot instantiate VespaRetriever (error that it takes only 1 argument but 4 were given)
rolling version back
### 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
from langchain.retrievers.vespa_retriever import VespaRetriever
vespa_query_body = {
"yql": 'select * from abstracts where userQuery() or ({targetHits:1}nearestNeighbor(paragraph_embeddings,q))',
'input.query(q)': 'embed(q)',
'query': 'q',
"hits": '3',
"ranking": "hybrid",
}
vespa_content_field = "paragraph_embeddings"
retriever = VespaRetriever(app=vespa_app, body=vespa_query_body, content_field=vespa_content_field)
### Expected behavior
retriever should instantiate but does not
|
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| 1,799,142,822 |
PR_kwDOIPDwls5VNrdy
| 7,546 |
Added Memory Parameter to SQL-Chain
|
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[] | null | 21 | 2023-07-11T14:59:47 | 2023-12-16T07:07:40 | 2023-07-31T19:16:52 |
CONTRIBUTOR
| null |
- Description: SQL Database Chain and Agent does not allow to pass in memory therefore added this feature with additional tests.
- Issue: #6918
- Tag maintainer: @hwchase17
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PR_kwDOIPDwls5VNiA-
| 7,545 |
fix cpal docs
|
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| 1,799,037,692 |
PR_kwDOIPDwls5VNUJ3
| 7,544 |
bump 230
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PR_kwDOIPDwls5VNT4Y
| 7,543 |
Update llama_index.py to new versions of llama-index
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[] | null | 1 | 2023-07-11T14:12:03 | 2023-07-11T14:21:45 | 2023-07-11T14:19:25 |
CONTRIBUTOR
| null |
- Description: Llama-index updated its files and the retrievers no longer worked. I also added the async version of `get_relevant_documents`
@rlancemartin, @eyurtsev
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| 1,799,011,449 |
I_kwDOIPDwls5rOrx5
| 7,542 |
Issue: Passing auth object to LLMRequestsChain
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[] | null | 0 | 2023-07-11T13:59:38 | 2023-07-14T12:38:25 | 2023-07-14T12:38:25 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Accessing many corporate resources requires special authentication, e.g. Kerberos.
The `requests` library supports passing an auth object, e.g.
`requests.get(url, auth=HttpNegotiateAuth(), verify=False)` to use SSPI.
We're able to pass a `requests_wrapper `to `LLMRequestsChain`, but it only allows changing headers, not the actual get method that is used.
### Suggestion:
Allow for more generic generic wrappers to be passed? Allow passing a requests-compatible auth object?
|
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PR_kwDOIPDwls5VNFd-
| 7,541 |
Update combined.py
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[] | null | 2 | 2023-07-11T13:40:10 | 2023-08-01T01:15:01 | 2023-08-01T01:15:01 |
CONTRIBUTOR
| null |
from my understanding, the `check_repeated_memory_variable` validator will raise an error if any of the variables in the `memories` list are repeated. However, the `load_memory_variables` method does not check for repeated variables. This means that it is possible for the `CombinedMemory` instance to return a dictionary of memory variables that contains duplicate values. This code will check for repeated variables in the `data` dictionary returned by the `load_memory_variables` method of each sub-memory. If a repeated variable is found, an error will be raised.
<!-- 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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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PR_kwDOIPDwls5VM5PY
| 7,540 |
clarify about api key
|
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[] | null | 2 | 2023-07-11T13:12:24 | 2023-07-11T20:46:07 | 2023-07-11T20:46:07 |
CONTRIBUTOR
| null |
I found it unclear, where to get the API keys for JinaChat. Mentioning this in the docstring should be helpful.
#7490
Twitter handle: benji1a
@delgermurun
|
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I_kwDOIPDwls5rORIf
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Importance of VectorStoreToolkit, vectorstore_agent and VectorStoreInfo in document based domain specific question answering
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[] | null | 6 | 2023-07-11T13:06:13 | 2023-10-23T16:07:02 | 2023-10-23T16:07:01 |
NONE
| null |
### Issue you'd like to raise.
I am trying to query the documents using the below stack
**Langchain+ embedding tool + vectore store + LLM model**
There are many tools and techniques for this in langchain including **load_qa_chain, RetrievalQA,VectorstoreIndexCreator,ConversationalRetrievalChain**. Those are already giving the good results(Not optimal)
But found one more technique including **VectorStoreInfo,VectorStoreToolkit and vectorstore_agent**
What is the advantage/Importance of this pipeline which using **VectorStoreInfo,VectorStoreToolkit and vectorstore_agent** over the other which doesnt follow this pipeline(use any of **load_qa_chain, RetrievalQA,VectorstoreIndexCreator,ConversationalRetrievalChain**)
### Suggestion:
_No response_
|
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feat:add DBGPTRetriever
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NONE
| null |
- Description: 1.add DBGPTRetriever implement 2.update dbgpt.mdx and dbgpt.ipynb for how to use DBGPTRetriever.
- Issue: None,
- Dependencies: None,
- Tag maintainer: @rlancemartin, @eyurtsev, have a look, thanks .
```
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
) -> List[Document]:
"""Get documents relevant for a query."""
try:
from pilot import EmbeddingEngine
except ImportError:
raise ImportError(
"You need to install `pip install db-gpt` to use this retriever."
)
embedding_engine = EmbeddingEngine(
model_name=self.embedding_model_path,
vector_store_config=self.vector_store_config,
)
docs = embedding_engine.similar_search(query, self.top_k)
return docs
```
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openai.error.InvalidRequestError: Resource not found
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[] | null | 3 | 2023-07-11T12:23:21 | 2023-10-17T16:05:04 | 2023-10-17T16:05:03 |
NONE
| null |
### System Info
Python 3.9.12
Langchain 0.0.229
OS Linux Mint 21.1
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am following this tutorial on structured output https://python.langchain.com/docs/modules/model_io/output_parsers/structured
I am passing my openai API key from config, I have made sure that it is being passed as I can see the output of the `chat_model` instance...
```python
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.chat_models import ChatOpenAI
from config import config
response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(name="source", description="source used to answer the user's question, should be a website.")
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
prompt = PromptTemplate(
template="answer the users question as best as possible.\n{format_instructions}\n{question}",
input_variables=["question"],
partial_variables={"format_instructions": format_instructions}
)
chat_model = ChatOpenAI(temperature=0, openai_api_key=config.OPENAI_API_KEY)
print(chat_model)
prompt = ChatPromptTemplate(
messages=[
HumanMessagePromptTemplate.from_template("answer the users question as best as possible.\n{format_instructions}\n{question}")
],
input_variables=["question"],
partial_variables={"format_instructions": format_instructions}
)
_input = prompt.format_prompt(question="what's the capital of france?")
print(_input.to_messages())
output = chat_model(_input.to_messages())
print(output)
print(output_parser.parse(output.content))
```
### Expected behavior
The expected output of the code should be
```json
{'answer': 'Paris', 'source': 'https://en.wikipedia.org/wiki/Paris'}
```
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I_kwDOIPDwls5rN7G-
| 7,535 |
Support for sparse tables
|
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[] | null | 1 | 2023-07-11T12:18:49 | 2023-10-17T16:05:09 | 2023-10-17T16:05:08 |
NONE
| null |
### Feature request
The sql agent should query in a manner that it gets **unique** values as sample data (metadata) instead of all values. Only then will it be able to understand which columns to query from. Otherwise, it might get confused between similar-sounding column names (ex: age, age_group)
### Motivation
Databases are typically very sparse (several columns are null). In such cases, the sql agent will perform poorly. The reason is it uses the InfoSQLDatabaseTool(sql_db_schema) to get all sample rows from the database. If the values are themselves null, then it doesn't get an accurate idea of what each column is supposed to contain. This would affect the query generation and the checking part too.
### Your contribution
I'm not so sure as of now.
|
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PR_kwDOIPDwls5VMeOw
| 7,534 |
[Minor] Remove tagger from spacy sentencizer
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[] | null | 2 | 2023-07-11T12:07:57 | 2023-07-11T21:18:31 | 2023-07-11T20:43:46 |
CONTRIBUTOR
| null |
@svlandeg gave me a tip for how to improve a bit on https://github.com/hwchase17/langchain/pull/7442 for some extra speed and memory gains. The tagger isn't needed for sentencization, so can be disabled too.
@baskaryan
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PR_kwDOIPDwls5VMci2
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Fast load conversationsummarymemory from existing summary
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[] | null | 2 | 2023-07-11T12:03:53 | 2023-08-01T01:14:12 | 2023-08-01T01:14:12 |
CONTRIBUTOR
| null |
- Description: Adds an optional buffer arg to the memory's from_messages() method. If provided the existing memory will be loaded instead of regenerating a summary from the loaded messages.
Why? If we have past messages to load from, it is likely we also have an existing summary. This is particularly helpful in cases where the chat is ephemeral and/or is backed by serverless where the chat history is not stored but where the updated chat history is passed back and forth between a backend/frontend.
Eg: Take a stateless qa backend implementation that loads messages on every request and generates a response — without this addition, each time the messages are loaded via from_messages, the summaries are recomputed even though they may have just been computed during the previous response. With this, the previously computed summary can be passed in and avoid:
1) spending extra $$$ on tokens, and
2) increased response time by avoiding regenerating previously generated summary.
Tag maintainer: @hwchase17
Twitter handle: https://twitter.com/ShantanuNair
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| 1,798,735,631 |
I_kwDOIPDwls5rNocP
| 7,532 |
Generating answers from LLM's pretrianed knowledge base, instead of from the embedded document.
|
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[] | null | 6 | 2023-07-11T11:35:56 | 2023-11-10T16:08:12 | 2023-11-10T16:08:11 |
NONE
| null |
### System Info
langchain==0.0.219
python 3.9
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
import os
import openai
import pinecone
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
directory = '/content/data'
def load_docs(directory):
loader = DirectoryLoader(directory)
documents = loader.load()
return documents
documents = load_docs(directory)
def split_docs(documents, chunk_size=1000, chunk_overlap=20):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
docs = text_splitter.split_documents(documents)
return docs
docs = split_docs(documents)
embeddings = OpenAIEmbeddings(model_name="ada")
pinecone.init(
api_key="pinecone api key",
environment="env"
)
index_name = "langchain-demo"
index = Pinecone.from_documents(docs, embeddings, index_name=index_name)
model_name = "gpt-4"
llm = OpenAI(model_name=model_name)
chain = load_qa_chain(llm, chain_type="stuff")
def get_similiar_docs(query, k=2, score=False):
if score:
similar_docs = index.similarity_search_with_score(query, k=k)
else:
similar_docs = index.similarity_search(query, k=k)
return similar_docs
def get_answer(query):
similar_docs = get_similiar_docs(query)
answer = chain.run(input_documents=similar_docs, question=query)
return answer
```
In the above code, If I ask any question it is answered from an outer world other than the document corpus.
### Expected behavior
If I am asked any domain-specific query, it should answer based on the embedded document corpus only. I am not expecting any outer-domain answer.
If the query is not related to the embedded document store, then it shouldn't answer anything, instead of searching and generating the answer from its own base pretrained knowledge.
|
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| 1,798,725,077 |
I_kwDOIPDwls5rNl3V
| 7,531 |
Issue: ConfluenceLoader 403 Forbidden Failed to parse Connect Session Auth Token
|
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[] | null | 3 | 2023-07-11T11:29:58 | 2023-07-12T06:21:31 | 2023-07-12T06:20:55 |
NONE
| null |
### Issue you'd like to raise.
I am trying to integrate confluence for open ai embedding and vector store using in memory doc-array.
I am sure this must not be issue Langchain but thought of posting here . Any pointer would really appreciated.
I created one free trial version account with atlassian.com and trying one POC with confluence pages.
from langchain.document_loaders import ConfluenceLoader .
loader = ConfluenceLoader(url="https://yogeshdeshmukh.atlassian.net/wiki",
token="XXXX")
documentLoaders = documentLoaders + loader.load(space_key="YYYY", include_attachments=False, limit=10)
As per logs it calls
[https://yogeshdeshmukh.atlassian.net:443](https://yogeshdeshmukh.atlassian.net/) "GET /wiki/rest/api/content?spaceKey=~YYYY&limit=10&status=current&expand=body.storage&type=page HTTP/1.1" 403 None
DEBUG:atlassian.rest_client:HTTP: GET rest/api/content -> 403 Forbidden
DEBUG:atlassian.rest_client:HTTP: Response text -> {"error": "Failed to parse Connect Session Auth Token"}
ERROR:atlassian.confluence:'message'
Traceback (most recent call last):
File "/Users/ydeshmukh/Library/Python/3.9/lib/python/site-packages/atlassian/confluence.py", line 3122, in raise_for_status
error_msg = j["message"]
Any idea do i need to provide some additional parameters . I tried with password but was failing later came to know password base basic auth deprecated hence register for token but that also failing .
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5VMOuL
| 7,530 |
Reuse the existing collection if configured properly in Qdrant.from_texts
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[] | null | 2 | 2023-07-11T11:28:34 | 2023-07-12T08:22:19 | 2023-07-11T20:24:36 |
CONTRIBUTOR
| null |
This PR changes the behavior of `Qdrant.from_texts` so the collection is reused if not requested to recreate it. Previously, calling `Qdrant.from_texts` or `Qdrant.from_documents` resulted in removing the old data which was confusing for many.
@rlancemartin, @eyurtsev
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I_kwDOIPDwls5rNdEU
| 7,529 |
$contain is not a valid operator in SelfQueryRetrieval
|
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[] | null | 4 | 2023-07-11T11:07:41 | 2023-10-23T17:24:37 | 2023-10-17T16:05:13 |
CONTRIBUTOR
| null |
### System Info
0.0.228
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code to reproduce:
```
embeddings = OpenAIEmbeddings(deployment=OPENAI_EMBEDDING_DEPLOYMENT_NAME,
model=OPENAI_EMBEDDING_MODEL_NAME,
chunk_size=1)
# create new index
#pinecone.create_index("langchain-self-retriever-demo", dimension=1536)
vectorstore = Pinecone.from_existing_index(index_name="cubigo", embedding=embeddings, namespace="vwProfilesMetadata")
metadata_field_info = [
AttributeInfo(
name="FirstName",
description="The first name of the resident",
type="string",
),
AttributeInfo(
name="LastName",
description="The last name of the resident",
type="string",
),
AttributeInfo(
name="Gender",
description="The gender of the resident",
type="string",
),
AttributeInfo(
name="Birthdate",
description="The birthdate of the resident or the date the resident was born",
type="Date"
),
AttributeInfo(
name="Birthplace",
description="The birthplace of the resident or the place the resident was born",
type="string"
),
AttributeInfo(
name="Hometown",
description="The town or city where the resident grew up",
type="string"
)
]
document_content_description = "The content of the document describes " \
"a resident of the facility, each document is a resident and it " \
"has all the information about the resident like FirstName," \
"LastName, RoleName, Gender, PhoneNumber, CellPhoneNumber, Address, " \
"Birthdate, Birthplace, Hometown, Education, CollegeName, PastOccupations, " \
"Veteran, NameOfSpouse, ReligiousPreferences, SpokenLanguages, " \
"ActiveLiveDescription, RetiredLiveDescription, Accomplishments, AnniversaryDate, " \
"YourTypicalDay, TalentsAndHobbies, InterestCategories, OtherInterestCategories," \
"FavoriteActor, FavoriteActress, FavoriteAnimal, FavoriteArtist, FavoriteAuthor, " \
"FavoriteBandMusicalArtist, FavoriteBook, FavoriteClimate, FavoriteColor, FavoriteCuisine, " \
"FavoriteDance, FavoriteDessert, FavoriteDrink, FavoriteFood, FavoriteFruit, FavoriteFutureTravelDestination, " \
"FavoriteGame, FavoriteMovie, FavoritePastTravelDestination, FavoriteSeasonOfTheYear, FavoriteSong, FavoriteSport, " \
"FavoriteSportsTeam, FavoriteTvShow, FavoriteVegetable"
user_input = get_text()
llm = AzureChatOpenAI(
openai_api_base=OPENAI_DEPLOYMENT_ENDPOINT,
openai_api_version=OPENAI_API_VERSION ,
deployment_name=OPENAI_DEPLOYMENT_NAME,
openai_api_key=OPENAI_API_KEY,
openai_api_type = OPENAI_API_TYPE ,
model_name=OPENAI_MODEL_NAME,
temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm, vectorstore, document_content_description, metadata_field_info, verbose=True, enable_limit=True
)
#response = retriever.get_relevant_documents(user_input)
chain = RetrievalQAWithSourcesChain.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
if user_input:
response = chain({"question": user_input})
```
```
Exception:
`
ApiException: (400) Reason: Bad Request HTTP response headers: HTTPHeaderDict({'content-type': 'application/json', 'date': 'Tue, 11 Jul 2023 11:04:33 GMT', 'x-envoy-upstream-service-time': '0', 'content-length': '68', 'server': 'envoy'}) HTTP response body: {"code":3,"message":"$contain is not a valid operator","details":[]
```}`
Question I am asking:
Who is interested in baking?
if I ask:
Who likes baking? Then no errors
### Expected behavior
Should get a clear response or no answer.
|
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I_kwDOIPDwls5rNZyl
| 7,528 |
Using Open source LLM models in SQL Chain
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NONE
| null |
### Using Open source LLM models in SQL Chain
Is it possible to use open source LLM models in SQL chain ?
I have tried using tapex/Flan models in SQL Chain, but getting a serialization error on dict[] classes.
Error:
```
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for SQLDatabaseChain
__root__ -> llm
**_value is not a valid dict (type=type_error.dict)_**
```
Are there any samples/snippets are available for using open source LLM models in SQL Chain ?
Sample code snippet I tried that is throwing the error:
```
tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base-finetuned-wtq")
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base-finetuned-wtq")
chain = SQLDatabaseChain(llm=model, database=db, verbose=True)
chain.run("context query ?")
```
### Suggestion:
_No response_
|
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| 1,798,612,838 |
I_kwDOIPDwls5rNKdm
| 7,527 |
SelfQueryRetriever, Add option to return all when user asks
|
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[] | null | 3 | 2023-07-11T10:21:55 | 2023-11-20T16:06:02 | 2023-11-20T16:06:01 |
CONTRIBUTOR
| null |
### Feature request
I tested the enable_limit to True and asking thins like
1. Get 3 residents who were born in xxx
2. Get 5 Residens who were bon in xxx
It works pretty well.
However in my use case, users can ask also: List all the residents who were born in xxx.
When questions like this are done, by default it will return only 4 documents and not all.
### Motivation
My use case requires sometimes to list all documents that match the criteria, not only 4.
### Your contribution
I am a beginner in langchain (only used it for 2 months), so not sure where in the code this can be fixed, but with the proper guidance I should be able to contribute (if somebody is willing to guide me)
|
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I_kwDOIPDwls5rMvuo
| 7,526 |
RetrievalQAWithSourcesChain acall does not write fromatted prompt to terminal if verbose=True
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[] | null | 3 | 2023-07-11T09:22:43 | 2023-07-11T11:21:36 | 2023-07-11T11:06:40 |
NONE
| null |
### System Info
python==3.10
langchain==0.0.169
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
Steps to reproduce:
1. Open the [example notebook](https://colab.research.google.com/drive/1ut3LVSSxsN_C52Pn1ceqWdHjSzhxuZol?usp=sharing)
2. Replace ```insert API key here```with your API key
3. Run all cells
### Expected behavior
Asynchronously calling the RetrievalQAWithSourcesChain with ```chain_type_kwargs = {"prompt": prompt, "verbose": True}``` should result in the same terminal output as the synchronous version instead of skipping "Prompt after formatting: ..."
```result = chain(query)``` output:
```
> Entering new StuffDocumentsChain chain...
> Entering new LLMChain chain...
Prompt after formatting:
PROMPT_AFTER_FORMATTING
> Finished chain.
> Finished chain.
```
Wrong ```result = await chain.acall(query)``` output:
```
> Entering new StuffDocumentsChain chain...
> Entering new LLMChain chain...
> Finished chain.
> Finished chain.
```
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| 1,798,476,485 |
PR_kwDOIPDwls5VLYUn
| 7,525 |
fix: openapi controller prompt, when bot is unable to resolve an api …
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[] | null | 1 | 2023-07-11T09:08:48 | 2023-08-01T00:56:44 | 2023-08-01T00:56:44 |
CONTRIBUTOR
| null |
…call, it needs retry
<!-- 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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,798,403,821 |
I_kwDOIPDwls5rMXbt
| 7,524 |
Specific name of the current chain is not displayed
|
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[] | null | 5 | 2023-07-11T08:28:40 | 2023-07-14T00:14:47 | 2023-07-14T00:14:47 |
CONTRIBUTOR
| null |
### System Info
LangChain v0.0.229, Python v3.10.12, Ubuntu 20.04.2 LTS
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I am encountering an issue where the specific name of the current chain is not being displayed in the console output, even though I have set 'verbose=True' in the MultiPromptChain and other Chains. When the program enters a new chain, it only prints 'Entering new chain...' without specifying the name of the chain. This makes it difficult to debug and understand which chain is currently being used. Could you please look into this issue and provide a way to display the name of the current chain in the console output? Thank you.
The output could be
```
> Entering new chain...
> Entering new chain...
lib/python3.10/site-packages/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
warnings.warn(
> Finished chain.
math: {'input': 'What is the derivative of a function?'}
> Entering new chain...
Prompt after formatting:
You are a very good mathematician. You are great at answering math questions. \nYou are so good because you are able to break down hard problems into their component parts, \nanswer the component parts, and then put them together to answer the broader question.
Here is a question:
What is the derivative of a function?
> Finished chain.
> Finished chain.
```
### Expected behavior
```
> Entering new MultiPromptChain chain...
> Entering new LLMRouterChain chain...
lib/python3.10/site-packages/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
warnings.warn(
> Finished chain.
math: {'input': 'What is the derivative of a function?'}
> Entering new LLMChain[math] chain...
Prompt after formatting:
You are a very good mathematician. You are great at answering math questions. \nYou are so good because you are able to break down hard problems into their component parts, \nanswer the component parts, and then put them together to answer the broader question.
Here is a question:
What is the derivative of a function?
> Finished chain.
> Finished chain.
```
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| 1,798,387,965 |
I_kwDOIPDwls5rMTj9
| 7,523 |
AttributeError: 'str' object has no attribute 'upsert' in Pinecone Hybrid Search
|
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[] | null | 3 | 2023-07-11T08:19:47 | 2023-10-18T16:06:03 | 2023-10-18T16:06:02 |
CONTRIBUTOR
| null |
### System Info
0.0.228
### Who can help?
@lbsnrs
### 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
Following tutorial here:
https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/pinecone_hybrid_search
```
bm25_encoder = BM25Encoder().default()
embed = OpenAIEmbeddings(deployment=OPENAI_EMBEDDING_DEPLOYMENT_NAME, model=OPENAI_EMBEDDING_MODEL_NAME, chunk_size=1)
retriever = PineconeHybridSearchRetriever(
embeddings=embed, sparse_encoder=bm25_encoder, index="cubigometadatanotindexed"
)
retriever.add_texts(["foo", "bar", "FirstName0003384 is a guy", "FirstName0003381 is a girl"])
result = retriever.get_relevant_documents("Who is FirstName0003381?")
```
I get this error:
```
AttributeError: 'str' object has no attribute 'upsert'
Traceback:
File "C:\Users\xx\anaconda3\envs\xxChatbotv3\Lib\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "C:\Users\xx\repos\xxChatbotv1\app\pages\Pinecone Hybrid Search.py", line 116, in <module>
main()
File "C:\Users\xx\repos\xxChatbotv1\app\pages\Pinecone Hybrid Search.py", line 112, in main
retriever.add_texts(["foo", "bar", "FirstName0003384 is a guy", "hello"])
File "C:\Users\xx\anaconda3\envs\zzChatbotv3\Lib\site-packages\langchain\retrievers\pinecone_hybrid_search.py", line 121, in add_texts
create_index(
File "C:\Users\xx\anaconda3\envs\zzChatbotv3\Lib\site-packages\langchain\retrievers\pinecone_hybrid_search.py", line 98, in create_index
index.upsert(vectors)
^^^^^^^^^^^^
```
### Expected behavior
The texts should be added to the index without error
|
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TypeError: issubclass() arg 1 must be a class
|
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[] | null | 21 | 2023-07-11T07:59:09 | 2023-10-18T16:06:08 | 2023-10-18T16:06:07 |
NONE
| null |
### Issue you'd like to raise.
Traceback (most recent call last):
File "D:\EmbeddingsSearch\llm-python\02b_llama_chroma.py", line 2, in <module>
from llama_index import SimpleDirectoryReader, StorageContext, GPTVectorStoreIndex
File "F:\Anaconda\lib\site-packages\llama_index\__init__.py", line 15, in <module>
from llama_index.embeddings.langchain import LangchainEmbedding
File "F:\Anaconda\lib\site-packages\llama_index\embeddings\__init__.py", line 4, in <module>
from llama_index.embeddings.langchain import LangchainEmbedding
File "F:\Anaconda\lib\site-packages\llama_index\embeddings\langchain.py", line 6, in <module>
from langchain.embeddings.base import Embeddings as LCEmbeddings
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\agents\__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\agents\agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\agents\tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\tools\__init__.py", line 3, in <module>
from langchain.tools.arxiv.tool import ArxivQueryRun
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\tools\arxiv\tool.py", line 12, in <module>
from langchain.utilities.arxiv import ArxivAPIWrapper
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\utilities\__init__.py", line 3, in <module>
from langchain.utilities.apify import ApifyWrapper
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\utilities\apify.py", line 5, in <module>
from langchain.document_loaders import ApifyDatasetLoader
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\document_loaders\__init__.py", line 44, in <module>
from langchain.document_loaders.embaas import EmbaasBlobLoader, EmbaasLoader
File "C:\Users\Leaper\AppData\Roaming\Python\Python310\site-packages\langchain\document_loaders\embaas.py", line 54, in <module>
class BaseEmbaasLoader(BaseModel):
File "pydantic\main.py", line 204, in pydantic.main.ModelMetaclass.__new__
File "pydantic\fields.py", line 488, in pydantic.fields.ModelField.infer
File "pydantic\fields.py", line 419, in pydantic.fields.ModelField.__init__
File "pydantic\fields.py", line 539, in pydantic.fields.ModelField.prepare
File "pydantic\fields.py", line 801, in pydantic.fields.ModelField.populate_validators
File "pydantic\validators.py", line 696, in find_validators
File "pydantic\validators.py", line 585, in pydantic.validators.make_typeddict_validator
File "pydantic\annotated_types.py", line 35, in pydantic.annotated_types.create_model_from_typeddict
File "pydantic\main.py", line 972, in pydantic.main.create_model
File "pydantic\main.py", line 204, in pydantic.main.ModelMetaclass.__new__
File "pydantic\fields.py", line 488, in pydantic.fields.ModelField.infer
File "pydantic\fields.py", line 419, in pydantic.fields.ModelField.__init__
File "pydantic\fields.py", line 534, in pydantic.fields.ModelField.prepare
File "pydantic\fields.py", line 638, in pydantic.fields.ModelField._type_analysis
File "F:\Anaconda\lib\typing.py", line 1158, in __subclasscheck__
return issubclass(cls, self.__origin__)
TypeError: issubclass() arg 1 must be a class
Process finished with exit code 1
### Suggestion:
_No response_
|
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| 1,798,297,796 |
PR_kwDOIPDwls5VKxVY
| 7,521 |
Adds OpenAI functions powered document metadata tagger
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[] | null | 3 | 2023-07-11T07:27:37 | 2023-07-13T05:12:42 | 2023-07-13T05:12:42 |
CONTRIBUTOR
| null |
Adds a new document transformer that automatically extracts metadata for a document based on an input schema. I also moved `document_transformers.py` to `document_transformers/__init__.py` to group it with this new transformer - it didn't seem to cause issues in the notebook, but let me know if I've done something wrong there.
Also had a linter issue I couldn't figure out:
```
MacBook-Pro:langchain jacoblee$ make lint
poetry run mypy .
docs/dist/conf.py: error: Duplicate module named "conf" (also at "./docs/api_reference/conf.py")
docs/dist/conf.py: note: See https://mypy.readthedocs.io/en/stable/running_mypy.html#mapping-file-paths-to-modules for more info
docs/dist/conf.py: note: Common resolutions include: a) using `--exclude` to avoid checking one of them, b) adding `__init__.py` somewhere, c) using `--explicit-package-bases` or adjusting MYPYPATH
Found 1 error in 1 file (errors prevented further checking)
make: *** [lint] Error 2
```
@rlancemartin @baskaryan
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PR_kwDOIPDwls5VKmVs
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Lost in the middle: We have been ordering documents the WRONG way. (for long context)
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CONTRIBUTOR
| null |
Motivation, it seems that when dealing with a long context and "big" number of relevant documents we must avoid using out of the box score ordering from vector stores.
See: https://arxiv.org/pdf/2306.01150.pdf
So, I added an additional parameter that allows you to reorder the retrieved documents so we can work around this performance degradation. The relevance respect the original search score but accommodates the lest relevant document in the middle of the context.
Extract from the paper (one image speaks 1000 tokens):
This seems to be common to all diff arquitectures. SO I think we need a good generic way to implement this reordering and run some test in our already running retrievers.
It could be that my approach is not the best one from the architecture point of view, happy to have a discussion about that.
For me this was the best place to introduce the change and start retesting diff implementations.
@rlancemartin, @eyurtsev
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| 1,798,231,461 |
I_kwDOIPDwls5rLtWl
| 7,519 |
Hybrid search indexing how to
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[] | null | 3 | 2023-07-11T06:48:37 | 2023-10-28T16:05:35 | 2023-10-28T16:05:34 |
CONTRIBUTOR
| null |
### Issue with current documentation:
https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/pinecone_hybrid_search
### Idea or request for content:
Its not clear on this page how to index data in pinecone for hybrid search, I am already indexing like this and it works, but for sparse values and the bm25encoder this is very confusing.
```
df = loadSqlData()
df.to_csv('profiles.csv', index=False)
# Iterate through DataFrame rows
# Time Complexity: O(n), where n is the number of rows in the DataFrame
for _, record in df.iterrows():
start_time = time.time()
# Get metadata for this record
# Time Complexity: O(1)
metadata = {
'IdentityId': str(record['IdentityId'])
}
st.write(f'Time taken for metadata extraction: {time.time() - start_time} seconds')
start_time = time.time()
# Split record text into chunks
# Time Complexity: O(m), where m is the size of the text
record_texts = text_splitter.split_text(record['content'])
st.write(f'Time taken for text splitting: {time.time() - start_time} seconds')
start_time = time.time()
# Create metadata for each chunk
# Time Complexity: O(k), where k is the number of chunks in the text
record_metadatas = [{
"chunk": j, "text": text, **metadata
} for j, text in enumerate(record_texts)]
st.write(f'Time taken for metadata dictionary creation: {time.time() - start_time} seconds')
start_time = time.time()
# Append chunks and metadata to current batches
# Time Complexity: O(1)
texts.extend(record_texts)
metadatas.extend(record_metadatas)
st.write(f'Time taken for data appending: {time.time() - start_time} seconds')
# If batch_limit is reached, upsert vectors
# Time Complexity: Depends on the upsert implementation
if len(texts) >= batch_limit:
start_time = time.time()
ids = [str(uuid4()) for _ in range(len(texts))]
# Simulating embedding and upserting here
Pinecone.from_texts(
texts,
embed,
index_name="xx",
metadatas=metadatas,
namespace="vwProfiles2")
texts = []
metadatas = []
st.write(f'Time taken for vector upsertion (simulated): {time.time() - start_time} seconds')
# Upsert any remaining vectors after the loop
# Time Complexity: Depends on the upsert implementation
if len(texts) > 0:
start_time = time.time()
ids = [str(uuid4()) for _ in range(len(texts))]
# Simulating embedding and upserting here
Pinecone.from_texts(
texts,
embed,
index_name="x",
metadatas=metadatas,
namespace="vwProfiles2")
st.write(f'Time taken for remaining vector upsertion (simulated): {time.time() - start_time} seconds')
st.write('Rows indexed: ', len(df))
```
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I_kwDOIPDwls5rLlb9
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AttributeError: 'tuple' object has no attribute 'page_content'
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[] | null | 4 | 2023-07-11T06:24:41 | 2023-12-30T23:53:45 | 2023-07-12T06:37:11 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.228
python 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have a pinecone index with information which I upserted from a SQL table like this:
```
df = loadSqlData()
df.to_csv('profiles.csv', index=False)
# Iterate through DataFrame rows
# Time Complexity: O(n), where n is the number of rows in the DataFrame
for _, record in df.iterrows():
start_time = time.time()
# Get metadata for this record
# Time Complexity: O(1)
metadata = {
'IdentityId': str(record['IdentityId'])
}
st.write(f'Time taken for metadata extraction: {time.time() - start_time} seconds')
start_time = time.time()
# Split record text into chunks
# Time Complexity: O(m), where m is the size of the text
record_texts = text_splitter.split_text(record['content'])
st.write(f'Time taken for text splitting: {time.time() - start_time} seconds')
start_time = time.time()
# Create metadata for each chunk
# Time Complexity: O(k), where k is the number of chunks in the text
record_metadatas = [{
"chunk": j, "text": text, **metadata
} for j, text in enumerate(record_texts)]
st.write(f'Time taken for metadata dictionary creation: {time.time() - start_time} seconds')
start_time = time.time()
# Append chunks and metadata to current batches
# Time Complexity: O(1)
texts.extend(record_texts)
metadatas.extend(record_metadatas)
st.write(f'Time taken for data appending: {time.time() - start_time} seconds')
# If batch_limit is reached, upsert vectors
# Time Complexity: Depends on the upsert implementation
if len(texts) >= batch_limit:
start_time = time.time()
ids = [str(uuid4()) for _ in range(len(texts))]
# Simulating embedding and upserting here
Pinecone.from_texts(
texts,
embed,
index_name="cubigo",
metadatas=metadatas,
namespace="vwProfiles2")
texts = []
metadatas = []
st.write(f'Time taken for vector upsertion (simulated): {time.time() - start_time} seconds')
# Upsert any remaining vectors after the loop
# Time Complexity: Depends on the upsert implementation
if len(texts) > 0:
start_time = time.time()
ids = [str(uuid4()) for _ in range(len(texts))]
# Simulating embedding and upserting here
Pinecone.from_texts(
texts,
embed,
index_name="cubigo",
metadatas=metadatas,
namespace="vwProfiles2")
st.write(f'Time taken for remaining vector upsertion (simulated): {time.time() - start_time} seconds')
st.write('Rows indexed: ', len(df))
```
And now I am trying to make a chatbot with my SQL Table, I dont want to use SQLToolkit or Agent as its very slow.
so I am trying to use the following code:
```
llm = AzureChatOpenAI(
openai_api_base=OPENAI_DEPLOYMENT_ENDPOINT,
openai_api_version=OPENAI_API_VERSION ,
deployment_name=OPENAI_DEPLOYMENT_NAME,
openai_api_key=OPENAI_API_KEY,
openai_api_type = OPENAI_API_TYPE ,
model_name=OPENAI_MODEL_NAME,
temperature=0)
embed = OpenAIEmbeddings(deployment=OPENAI_EMBEDDING_DEPLOYMENT_NAME, model=OPENAI_EMBEDDING_MODEL_NAME, chunk_size=1)
user_input = get_text()
vectorstore = Pinecone.from_existing_index("cubigo",embedding=embed, namespace="vwProfiles2")
docs =vectorstore.similarity_search_with_score(user_input, k=250, namespace="vwProfiles2") #Who is from Bransk
vectordb = Pinecone.from_documents(documents=docs,
embedding=embed)
qa = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=vectordb.as_retriever()
)
response = qa.run(user_input)
```
But I get this error
AttributeError: 'tuple' object has no attribute 'page_content'
```
File "C:\Users\xx\repos\xxChatbotv1\app\pages\07Chat With Pinecone Directly.py", line 100, in main
vectordb = Pinecone.ncia\repos\xxChatbotv1\app\pages\07Chat With Pinecone Directly.py", line 100, in main
vectordb = Pinecone.from_documents(documents=docs,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\xx\anaconda3\envs\xxChatbotv3\Lib\site-packages\langchain\vectorstores\base.py", line 334, in from_documents
texts = [d.page_content for d in documents]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\xx\anaconda3\envs\xxChatbotv3\Lib\site-packages\langchain\vectorstores\base.py", line 334, in <listcomp>
texts = [d.page_content for d in documents]
^^^^^^^^^^^^^^from_documents(documents=docs,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\xx\anaconda3\envs\xxChatbotv3\Lib\site-packages\langchain\vectorstores\base.py", line 334, in from_documents
texts = [d.page_content for d in documents]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\xx\anaconda3\envs\xxChatbotv3\Lib\site-packages\langchain\vectorstores\base.py", line 334, in <listcomp>
texts = [d.page_content for d in documents]
^^^^^^^^^^^^^^
```
### Expected behavior
response in plain english?
|
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PR_kwDOIPDwls5VKVy1
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Add Google GenAppBuilder as Tools
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NONE
| null |
<!-- Thank you for contributing to LangChain!
- Description: Add the support of Google AppBuilder as tools.
- Dependencies: None
- Twitter handle: @MiquelJulien
Maintainer responsibilities:
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- Models / Prompts: @hwchase17, @baskaryan
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- Tracing / Callbacks: @agola11
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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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PR_kwDOIPDwls5VKPYF
| 7,515 |
Create the partition if provided
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PR_kwDOIPDwls5VKMSj
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Minor update to reference other sql tool by tool names instead of hard coded string.
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[] | null | 4 | 2023-07-11T05:27:06 | 2023-07-11T21:44:24 | 2023-07-11T21:44:23 |
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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Currently there are 4 tools in SQL agent-toolkits, and 2 of them have reference to the other 2.
This PR change the reference from hard coded string to `{tool.name}`
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PR_kwDOIPDwls5VKCgD
| 7,512 |
Add ConcurrentLoader
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[] | null | 1 | 2023-07-11T04:41:30 | 2023-08-01T00:56:31 | 2023-08-01T00:56:31 |
CONTRIBUTOR
| null |
Works just like the GenericLoader but concurrently for those who choose to optimize their workflow.
@rlancemartin @eyurtsev
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PR_kwDOIPDwls5VJ61A
| 7,511 |
Update chat agents' output parser to extract action by regex
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[] | null | 8 | 2023-07-11T04:01:12 | 2023-08-04T01:47:44 | 2023-07-12T07:12:02 |
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: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
Currently `ChatOutputParser` extracts actions by splitting the text on "```", and then load the second part as a json string.
But sometimes the LLM will wrap the action in markdown code block like:
````markdown
```json
{
"action": "foo",
"action_input": "bar"
}
```
````
Splitting text on "```" will cause `OutputParserException` in such case.
This PR changes the behaviour to extract the `$JSON_BLOB` by regex, so that it can handle both ` ``` ``` ` and ` ```json ``` `
@hinthornw
|
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| 1,797,955,368 |
PR_kwDOIPDwls5VJm6o
| 7,510 |
Fix broken link from Marqo Ecosystem
|
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[] | null | 2 | 2023-07-11T02:28:57 | 2023-07-11T21:15:15 | 2023-07-11T21:15:15 |
CONTRIBUTOR
| null |
Small fix to a link from the Marqo page in the ecosystem.
The link was not updated correctly when the documentation structure changed to html pages instead of links to notebooks.
|
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I_kwDOIPDwls5rKbpY
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Langchain Import Issue
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[] | null | 18 | 2023-07-11T01:12:10 | 2023-10-18T16:06:13 | 2023-10-18T16:06:12 |
NONE
| null |
### System Info
Python 3.10.8
Langchain==0.0.229
AWS Sagemaker Studio w/ **PyTorch 2.0.0 Python 3.10 GPU Optimized** image
### Who can help?
@hwchase17 or @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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Was working fine in a Jupyter Notebook in AWS Sagemaker Studio for the past few weeks but today running into an issue with no code changes... import chain issue?
!pip install langchain openai chromadb tiktoken pypdf unstructured pdf2image;
from langchain.document_loaders import TextLoader
Results in:
```---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[10], line 1
----> 1 from langchain.document_loaders import TextLoader
2 docLoader = TextLoader('./docs/nlitest.txt', encoding='utf8')
3 document = docLoader.load()
File /opt/conda/lib/python3.10/site-packages/langchain/__init__.py:6
3 from importlib import metadata
4 from typing import Optional
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
7 from langchain.cache import BaseCache
8 from langchain.chains import (
9 ConversationChain,
10 LLMBashChain,
(...)
18 VectorDBQAWithSourcesChain,
19 )
File /opt/conda/lib/python3.10/site-packages/langchain/agents/__init__.py:2
1 """Interface for agents."""
----> 2 from langchain.agents.agent import (
3 Agent,
4 AgentExecutor,
5 AgentOutputParser,
6 BaseMultiActionAgent,
7 BaseSingleActionAgent,
8 LLMSingleActionAgent,
9 )
10 from langchain.agents.agent_toolkits import (
11 create_csv_agent,
12 create_json_agent,
(...)
21 create_vectorstore_router_agent,
22 )
23 from langchain.agents.agent_types import AgentType
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:25
17 from langchain.callbacks.base import BaseCallbackManager
18 from langchain.callbacks.manager import (
19 AsyncCallbackManagerForChainRun,
20 AsyncCallbackManagerForToolRun,
(...)
23 Callbacks,
24 )
---> 25 from langchain.chains.base import Chain
26 from langchain.chains.llm import LLMChain
27 from langchain.input import get_color_mapping
File /opt/conda/lib/python3.10/site-packages/langchain/chains/__init__.py:3
1 """Chains are easily reusable components which can be linked together."""
2 from langchain.chains.api.base import APIChain
----> 3 from langchain.chains.api.openapi.chain import OpenAPIEndpointChain
4 from langchain.chains.combine_documents.base import AnalyzeDocumentChain
5 from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
File /opt/conda/lib/python3.10/site-packages/langchain/chains/api/openapi/chain.py:17
15 from langchain.requests import Requests
16 from langchain.schema.language_model import BaseLanguageModel
---> 17 from langchain.tools.openapi.utils.api_models import APIOperation
20 class _ParamMapping(NamedTuple):
21 """Mapping from parameter name to parameter value."""
File /opt/conda/lib/python3.10/site-packages/langchain/tools/__init__.py:11
4 from langchain.tools.azure_cognitive_services import (
5 AzureCogsFormRecognizerTool,
6 AzureCogsImageAnalysisTool,
7 AzureCogsSpeech2TextTool,
8 AzureCogsText2SpeechTool,
9 )
10 from langchain.tools.base import BaseTool, StructuredTool, Tool, tool
---> 11 from langchain.tools.bing_search.tool import BingSearchResults, BingSearchRun
12 from langchain.tools.brave_search.tool import BraveSearch
13 from langchain.tools.convert_to_openai import format_tool_to_openai_function
File /opt/conda/lib/python3.10/site-packages/langchain/tools/bing_search/__init__.py:3
1 """Bing Search API toolkit."""
----> 3 from langchain.tools.bing_search.tool import BingSearchResults, BingSearchRun
5 __all__ = ["BingSearchRun", "BingSearchResults"]
File /opt/conda/lib/python3.10/site-packages/langchain/tools/bing_search/tool.py:10
5 from langchain.callbacks.manager import (
6 AsyncCallbackManagerForToolRun,
7 CallbackManagerForToolRun,
8 )
9 from langchain.tools.base import BaseTool
---> 10 from langchain.utilities.bing_search import BingSearchAPIWrapper
13 class BingSearchRun(BaseTool):
14 """Tool that adds the capability to query the Bing search API."""
File /opt/conda/lib/python3.10/site-packages/langchain/utilities/__init__.py:3
1 """General utilities."""
2 from langchain.requests import TextRequestsWrapper
----> 3 from langchain.utilities.apify import ApifyWrapper
4 from langchain.utilities.arxiv import ArxivAPIWrapper
5 from langchain.utilities.awslambda import LambdaWrapper
File /opt/conda/lib/python3.10/site-packages/langchain/utilities/apify.py:5
1 from typing import Any, Callable, Dict, Optional
3 from pydantic import BaseModel, root_validator
----> 5 from langchain.document_loaders import ApifyDatasetLoader
6 from langchain.document_loaders.base import Document
7 from langchain.utils import get_from_dict_or_env
File /opt/conda/lib/python3.10/site-packages/langchain/document_loaders/__init__.py:44
39 from langchain.document_loaders.duckdb_loader import DuckDBLoader
40 from langchain.document_loaders.email import (
41 OutlookMessageLoader,
42 UnstructuredEmailLoader,
43 )
---> 44 from langchain.document_loaders.embaas import EmbaasBlobLoader, EmbaasLoader
45 from langchain.document_loaders.epub import UnstructuredEPubLoader
46 from langchain.document_loaders.evernote import EverNoteLoader
File /opt/conda/lib/python3.10/site-packages/langchain/document_loaders/embaas.py:54
50 bytes: str
51 """The base64 encoded bytes of the document to extract text from."""
---> 54 class BaseEmbaasLoader(BaseModel):
55 """Base class for embedding a model into an Embaas document extraction API."""
57 embaas_api_key: Optional[str] = None
File /opt/conda/lib/python3.10/site-packages/pydantic/main.py:204, in pydantic.main.ModelMetaclass.__new__()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:488, in pydantic.fields.ModelField.infer()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:419, in pydantic.fields.ModelField.__init__()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:539, in pydantic.fields.ModelField.prepare()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:801, in pydantic.fields.ModelField.populate_validators()
File /opt/conda/lib/python3.10/site-packages/pydantic/validators.py:696, in find_validators()
File /opt/conda/lib/python3.10/site-packages/pydantic/validators.py:585, in pydantic.validators.make_typeddict_validator()
File /opt/conda/lib/python3.10/site-packages/pydantic/annotated_types.py:35, in pydantic.annotated_types.create_model_from_typeddict()
File /opt/conda/lib/python3.10/site-packages/pydantic/main.py:972, in pydantic.main.create_model()
File /opt/conda/lib/python3.10/site-packages/pydantic/main.py:204, in pydantic.main.ModelMetaclass.__new__()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:488, in pydantic.fields.ModelField.infer()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:419, in pydantic.fields.ModelField.__init__()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:534, in pydantic.fields.ModelField.prepare()
File /opt/conda/lib/python3.10/site-packages/pydantic/fields.py:638, in pydantic.fields.ModelField._type_analysis()
File /opt/conda/lib/python3.10/typing.py:1158, in _SpecialGenericAlias.__subclasscheck__(self, cls)
1156 return issubclass(cls.__origin__, self.__origin__)
1157 if not isinstance(cls, _GenericAlias):
-> 1158 return issubclass(cls, self.__origin__)
1159 return super().__subclasscheck__(cls)
TypeError: issubclass() arg 1 must be a class ```
### Expected behavior
The module should import with no error.
|
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PR_kwDOIPDwls5VJSsm
| 7,508 |
Split labeled criteria evaluator to new class
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[] | null | 1 | 2023-07-11T00:32:53 | 2023-07-12T23:20:28 | 2023-07-12T23:20:26 |
COLLABORATOR
| null |
- Add an enum
- Rm support for multi-criteria in single evaluator
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Minor edits to QA docs
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closed
| false | null |
[] | null | 1 | 2023-07-11T00:09:46 | 2023-07-11T05:15:06 | 2023-07-11T05:15:06 |
COLLABORATOR
| null |
Small clean-ups
|
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