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https://api.github.com/repos/langchain-ai/langchain/issues/6754
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| 1,774,480,128 |
I_kwDOIPDwls5pxGsA
| 6,754 |
`RetrievalQAWithSourcesChain` with `max_tokens_limit` throws error `Requested tokens exceed context window`
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[] | null | 1 | 2023-06-26T10:47:36 | 2023-10-02T16:05:29 | 2023-10-02T16:05:28 |
NONE
| null |
### System Info
Ubuntu 22.04.2
Python 3.10.11
langchain 0.0.215
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from pathlib import Path
from langchain import LlamaCpp
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Weaviate
model_dir = Path.home() / 'models'
llama_path = model_dir / 'llama-7b.ggmlv3.q4_0.bin'
assert llama_path.exists()
encoder = LlamaCppEmbeddings(model_path=str(llama_path))
encoder.client.verbose = False
readme_path = Path(__file__).parent.parent / 'README.md'
loader = UnstructuredMarkdownLoader(str(readme_path))
data = loader.load()
text_splitter = CharacterTextSplitter(
separator="\n\n",
chunk_size=10,
chunk_overlap=2,
length_function=len,
)
texts = text_splitter.split_documents(data)
db = Weaviate.from_documents(texts, encoder, weaviate_url='http://localhost:8080', by_text=False)
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
llm = LlamaCpp(model_path=str(llama_path), temperature=0, callback_manager=callback_manager, stop=[], verbose=False)
llm.client.verbose = False
chain = RetrievalQAWithSourcesChain.from_chain_type(llm, chain_type="stuff", retriever=db.as_retriever(),
reduce_k_below_max_tokens=True, max_tokens_limit=512)
query = "How do I install this package?"
chain({"question": query})
```
### Expected behavior
When setting `max_tokens_limit` I expect it to be the limit for the final prompt passed to the llm
Seeing this error message is very confusing after checking that the question and loaded source documents do not reach the token limit
When `BaseQAWithSourcesChain.from_llm` is called, it uses a long `combine_prompt_template` by default, which in the case of LlamaCpp is already over the token limit
I would expect `max_tokens_limit` to apply to the full prompt, or to receive an error message explaining that the limit was breached because of the template, and ideally an example of how to alter the template
|
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| 1,774,468,457 |
I_kwDOIPDwls5pxD1p
| 6,753 |
path_params and url format not work
|
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[] | null | 1 | 2023-06-26T10:39:55 | 2023-10-02T16:05:34 | 2023-10-02T16:05:33 |
CONTRIBUTOR
| null |
### System Info
Python 3.9.6
_**Requirement**_
aiohttp==3.8.4
aiosignal==1.3.1
async-timeout==4.0.2
attrs==23.1.0
certifi==2023.5.7
charset-normalizer==3.1.0
dataclasses-json==0.5.8
docopt==0.6.2
frozenlist==1.3.3
idna==3.4
langchain==0.0.215
langchainplus-sdk==0.0.17
marshmallow==3.19.0
marshmallow-enum==1.5.1
multidict==6.0.4
mypy-extensions==1.0.0
numexpr==2.8.4
numpy==1.25.0
openai==0.27.8
openapi-schema-pydantic==1.2.4
packaging==23.1
pipreqs==0.4.13
pydantic==1.10.9
PyYAML==6.0
requests==2.31.0
SQLAlchemy==2.0.17
tenacity==8.2.2
tqdm==4.65.0
typing-inspect==0.9.0
typing_extensions==4.6.3
urllib3==2.0.3
yarg==0.1.9
yarl==1.9.2
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Endpoint:
**URL**: https://api.gopluslabs.io//api/v1/token_security/{chain_id}
**arguments**:
<img width="593" alt="image" src="https://github.com/hwchase17/langchain/assets/24714804/b2d1226d-2d0b-45d8-9400-7f420d463f38">
In openapi.py: 160
<img width="596" alt="image" src="https://github.com/hwchase17/langchain/assets/24714804/06d70353-47de-4e32-90c3-7f5f6c316047">
After the _format_url, url doesn't change, the result of printer is also https://api.gopluslabs.io//api/v1/token_security/{chain_id}
### Expected behavior
Expect URL and path parameters to be properly combined after formatting like:
**https://api.gopluslabs.io//api/v1/token_security/1**
|
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I_kwDOIPDwls5pw458
| 6,752 |
from langchain.utilities import RequestsWrapper ImportError: cannot import name 'RequestsWrapper' from 'langchain.utilities'Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
|
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[] | null | 2 | 2023-06-26T10:13:50 | 2023-10-02T16:05:39 | 2023-10-02T16:05:38 |
NONE
| null |
### Issue you'd like to raise.
_No response_
### Suggestion:
_No response_
|
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I_kwDOIPDwls5pwsl4
| 6,751 |
Weaviate.from_documents throws PosixPath is not JSON serializable when documents loaded via Pathlib
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[] | null | 2 | 2023-06-26T09:48:43 | 2023-10-02T16:05:44 | 2023-10-02T16:05:43 |
NONE
| null |
### System Info
Ubuntu 22.04.2
Python 3.10.11
langchain 0.0.215
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from pathlib import Path
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Weaviate
model_dir = Path.home() / 'models'
llama_path = model_dir / 'llama-7b.ggmlv3.q4_0.bin'
encoder = LlamaCppEmbeddings(model_path=str(llama_path))
readme_path = Path(__file__).parent.parent / 'README.md'
loader = UnstructuredMarkdownLoader(readme_path)
data = loader.load()
text_splitter = CharacterTextSplitter(
separator="\n\n",
chunk_size=10,
chunk_overlap=2,
length_function=len,
)
texts = text_splitter.split_documents(data)
db = Weaviate.from_documents(texts, encoder, weaviate_url='http://localhost:8080', by_text=False)
```
### Expected behavior
The `UnstructuredMarkdownLoader` loads the metadata as a `PosixPath` object
`Weaviate.from_documents` then throws an error because it can't post this metadata, as `PosixPath` is not serializable
If I change `loader = UnstructuredMarkdownLoader(readme_path)` to `loader = UnstructuredMarkdownLoader(str(readme_path))` then the metadata is loaded as a string, and the posting to Weaviate works
I would expect `UnstructuredMarkdownLoader` to have the same behaviour when I pass it a string or a path like object
I would expect Weaviate to handle serialising a path like object to a string
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| 1,774,318,073 |
I_kwDOIPDwls5pwfH5
| 6,749 |
Is there a way to improve the faiss index loading speed?
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[] | null | 2 | 2023-06-26T09:17:14 | 2023-11-02T09:53:30 | 2023-10-02T16:05:48 |
NONE
| null |
### Issue you'd like to raise.
The Faiss index is too large, the loading time is very long, and the experience is not good. Is there any way to optimize it?
### Suggestion:
_No response_
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Issue: ConversationBufferMemory stopped working
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[] | null | 10 | 2023-06-26T09:14:44 | 2023-10-07T16:06:05 | 2023-10-07T16:06:04 |
NONE
| null |
Hello everyone.
Oddly enough, I've recently run into a problem with memory.
In the first version, I had no issues, but now it has stopped working. It's as though my agent has Alzheimer's disease.
Does anyone have any suggestions as to why it might have stopped working?
There doesn't seem to be any error message or any apparent reason. Thank you!
I already tried reinstalling chromedb.
```
def agent(tools):
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613")
agent_kwargs = {
"user name is bob": [MessagesPlaceholder(variable_name="chat_history")],
}
template = """This is a conversation between a human and a bot:
{chat_history}
Write a summary of the conversation for {input}:
"""
prompt = PromptTemplate(input_variables=["input", "chat_history"], template=template)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
#agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)
agent_chain=initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, prompt = prompt, verbose=True, agent_kwargs=agent_kwargs, memory=memory, max_iterations=5, early_stopping_method="generate")
return agent_chain
```
Perhaps this error is realated
´´´
WARNING:root:Failed to load default session, using empty session: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
Failed to load default session, using empty session: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))
´´´
´´´
WARNING:root:Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connectio
n.HTTPConnection object at 0x7f1458a82770>: Failed to establish a new connection: [Errno 111] Connection refused')) Failed to persist run: HTTPConnectionPool(host='localhost', port=8000): Max retries exceeded with url: /chain-runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnect
ion object at 0x7f1458a82770>: Failed to establish a new connection: [Errno 111] Connection refused')) I'm sorry, but I don't have access to personal information.
´´´
|
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I_kwDOIPDwls5pwC0x
| 6,747 |
Issue: Entity extraction using custom rules
|
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[] | null | 9 | 2023-06-26T08:13:23 | 2023-10-05T16:08:07 | 2023-10-05T16:08:06 |
NONE
| null |
### Issue you'd like to raise.
I would like to perform a query on a database using natural language. However, running direct queries is not possible, and I have to do it via an API. For that, given a sentence, I'd like to extract some custom entities from it.
For example, if the sentence is: "How many more than 20 years old male users viewed a page or logged in in the last 30 days?"
The entities are:
```
<gender, equals, male>,
<age, greater than, 20>,
<event name, equals, view page>,
<event name, equals, login>,
<event timestamp, more than, 30 days>
```
The first element of each entity (triplet) comes from the list of columns
The second element is inferred from context (nature of the operator if it's a single value or array to compare with)
The third element is also inferred from the context and must belong to the chosen column (first element)
I'm not able to restrict either of these elements for the entity. I'd like an agent first to check all the columns that are available, choose one and view their unique values. Once it gets that, either choose that column (first element) and value (third element) or look again and repeat these steps.
Any help on this would be great!
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5T43ZD
| 6,746 |
Fixed caching bug for Multiple Caching types by correctly checking types
|
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[] | null | 2 | 2023-06-26T08:03:45 | 2023-06-26T08:14:33 | 2023-06-26T08:14:32 |
CONTRIBUTOR
| null |
- Fixed an issue where some caching types check the wrong types, hence not allowing caching to work
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
|
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I_kwDOIPDwls5pv5Fb
| 6,745 |
LLM中文应用交流微信群
|
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[] | null | 0 | 2023-06-26T07:53:56 | 2023-06-26T23:51:49 | 2023-06-26T23:51:49 |
NONE
| null |
LLM中文应用和技术交流群，如果二维码过期可加微信备注LLM应用：yydsa0007
|
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I_kwDOIPDwls5pvjo9
| 6,744 |
SitemapLoader is not working verify error from module
|
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[] | null | 1 | 2023-06-26T07:02:41 | 2023-10-02T16:06:04 | 2023-10-02T16:06:03 |
NONE
| null |
### System Info
langchain version i got from
!pip install langchain
import nest_asyncio
nest_asyncio.apply()
from langchain.document_loaders.sitemap import SitemapLoader
SitemapLoader.requests_per_second = 2
# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issue
#SitemapLoader.requests_kwargs = {'verify':True}
loader = SitemapLoader(
"https://www.infoblox.com/sitemap_index.xml",
)
docs = loader.load() #this is where it fails it works with version
TypeError Traceback (most recent call last)
[<ipython-input-5-609988fd11f7>](https://localhost:8080/#) in <cell line: 13>()
11 "https://www.infoblox.com/sitemap_index.xml",
12 )
---> 13 docs = loader.load()
2 frames
[/usr/local/lib/python3.10/dist-packages/langchain/document_loaders/web_base.py](https://localhost:8080/#) in _scrape(self, url, parser)
186 self._check_parser(parser)
187
--> 188 html_doc = self.session.get(url, verify=self.verify, **self.requests_kwargs)
189 html_doc.encoding = html_doc.apparent_encoding
190 return BeautifulSoup(html_doc.text, parser)
TypeError: requests.sessions.Session.get() got multiple values for keyword argument 'verify'
#this was working in older version
!pip install langchain==0.0.189
thanks
nick
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
follow the code above
### Expected behavior
it should run not crash
|
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| 1,773,971,733 |
I_kwDOIPDwls5pvKkV
| 6,743 |
id type in SupabaseVectorStore doesn't match SQL column
|
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[] | null | 9 | 2023-06-26T06:17:47 | 2023-10-10T03:06:10 | 2023-07-08T00:56:17 |
CONTRIBUTOR
| null |
### System Info
LangChain version: 0.0.214
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Same as [Supabase docs](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/supabase) with the exact table and function created, where `id` column has type of bigint.
```Python
from supabase.client import Client, create_client
from langchain.embeddings import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import SupabaseVectorStore
loader = TextLoader("../../../state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
supabase_client: Client = create_client(
supabase_url=os.getenv("SUPABASE_URL"),
supabase_key=os.getenv("SUPABASE_SERVICE_KEY"),
)
supabase_vector_store = SupabaseVectorStore.from_documents(
documents=docs,
client=supabase_client,
embedding=embeddings,
)
```
Got
> APIError: {'code': '22P02', 'details': None, 'hint': None, 'message': 'invalid input syntax for type bigint: "64f03aff-0c0e-4f24-91e2-e01fcaxxxxxx"'}
### Expected behavior
Successfully insert and embed the split docs into Supabase.
To be helpful, I believe it was introduced by [this commit](https://github.com/hwchase17/langchain/commit/be02572d586bcb33fffe89c37b81d5ba26762bec) regarding the List[str] type `ids`. But not sure if it was intended with docs not updated yet, or otherwise.
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I_kwDOIPDwls5puqit
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Getting "ValueError: Unsupported chat history format:" while using ConversationalRetrievalChain with memory type VectorStoreRetrieverMemory
|
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[] | null | 8 | 2023-06-26T04:47:59 | 2023-10-24T16:07:18 | 2023-10-24T16:07:17 |
NONE
| null |
### System Info
LangChain-0.0.207, Windows, Python-3.9.16
Memory (VectorStoreRetrieverMemory) Settings:
dimension = 768
index = faiss.IndexFlatL2(dimension)
embeddings = HuggingFaceEmbeddings()
vectorstore = FAISS(embeddings.embed_query, index, InMemoryDocstore({}), {})
retriever = vectorstore.as_retriever(search_kwargs=dict(k=3))
memory = VectorStoreRetrieverMemory(
memory_key="chat_history",
return_docs=True,
retriever=retriever,
return_messages=True,
)
ConversationalRetrievalChain Settings:
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\
Make sure to avoid using any unclear pronouns.
Chat History:
{chat_history}
(You do not need to use these pieces of information if not relevant)
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate(
input_variables=["chat_history", "question"], template=_template
)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(
llm, chain_type="refine"
) # stuff map_reduce refine
qa = ConversationalRetrievalChain(
retriever=chroma.as_retriever(search_kwargs=dict(k=5)),
memory=memory,
combine_docs_chain=doc_chain,
question_generator=question_generator,
return_source_documents=True,
# verbose=True,
)
responses = qa({"question": user_input})
ISSUE: At first cal, it is giving results. BUT in second call, it is throwing an error as follows:
ValueError: Unsupported chat history format: <class 'langchain.schema.Document'>. Full chat history:
What am I doing wrong here?
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Memory (VectorStoreRetrieverMemory) Settings:
dimension = 768
index = faiss.IndexFlatL2(dimension)
embeddings = HuggingFaceEmbeddings()
vectorstore = FAISS(embeddings.embed_query, index, InMemoryDocstore({}), {})
retriever = vectorstore.as_retriever(search_kwargs=dict(k=3))
memory = VectorStoreRetrieverMemory(
memory_key="chat_history",
return_docs=True,
retriever=retriever,
return_messages=True,
)
2. ConversationalRetrievalChain Settings:
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.\
Make sure to avoid using any unclear pronouns.
Chat History:
{chat_history}
(You do not need to use these pieces of information if not relevant)
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate(
input_variables=["chat_history", "question"], template=_template
)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(
llm, chain_type="refine"
) # stuff map_reduce refine
qa = ConversationalRetrievalChain(
retriever=chroma.as_retriever(search_kwargs=dict(k=5)),
memory=memory,
combine_docs_chain=doc_chain,
question_generator=question_generator,
return_source_documents=True,
# verbose=True,
)
responses = qa({"question": user_input})
ISSUE: At first cal, it is giving results. BUT in second call, it is throwing an error as follows:
ValueError: Unsupported chat history format: <class 'langchain.schema.Document'>. Full chat history:
What am I doing wrong here?
### Expected behavior
Chat history should be injected in chain
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AttributeError: 'OpenAI' object has no attribute 'predict'
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[] | null | 9 | 2023-06-26T04:01:06 | 2023-08-31T10:13:23 | 2023-06-28T02:37:07 |
NONE
| null |
### System Info
Langchain Version: 0.0.74
openai Version: 0.27.8
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms import OpenAI
llm = OpenAI(openai_api_key=INSERT_API_KEY, temperature=0.9)
llm.predict("What would be a good company name for a company that makes colorful socks?")
### Expected behavior
I was following the langchain quickstart guide, expected to see something similar to the output.
|
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PR_kwDOIPDwls5T3nxB
| 6,739 |
20230626
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[] | null | 1 | 2023-06-26T03:52:48 | 2023-06-26T07:36:49 | 2023-06-26T07:36:49 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,773,774,517 |
PR_kwDOIPDwls5T3mCa
| 6,738 |
V215 sqlalchemyfix
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NONE
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PR_kwDOIPDwls5T3h0u
| 6,737 |
Myscale/improve string pattern match
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[] | null | 2 | 2023-06-26T03:09:11 | 2023-09-07T09:42:51 | 2023-09-07T09:42:51 |
CONTRIBUTOR
| null |
- Description: Improved prompts for comparator `LIKE` and `CONTAIN`. They are ambiguous under some circumstances like "Answer this questions using papers published by Geoffrey Hinton". In that example LLM tends to use `LIKE` instead of `CONTAIN` to column `authors`, which is actually a column with type list of strings.
We revised the prompt to address this problem:
1. Changed prompt for comparator `LIKE` to `string_pattern_like`
2. Changed prompt for comparator `CONTAIN` to `list_contain`
@hwchase17
<!-- 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,
3. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
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Fixed bug in AnalyticDB Vector Store caused by upgrade SQLAlchemy version
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CONTRIBUTOR
| null |
Fixed bug in AnalyticDB VectorStore caused by the SQLAlchemy upgrade version to 1.4.47
The new version no longer supports the `connection.commit()` function, resulting in the error: AttributeError: 'Connection' object has no attribute 'commit'. Replace it with `with conn.begin():`.
This commit is related to VectorStores. @rlancemartin @eyurtsev
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PR_kwDOIPDwls5T3aV_
| 6,735 |
Update the token_max value in mp_reduce
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[] | null | 8 | 2023-06-26T02:30:57 | 2023-11-07T03:56:07 | 2023-11-07T03:56:06 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: When I use the gpt-3.5-turbo-16k model, I often encounter this ValueError 'A single document was so long it could not be combined' due to the small token_max.I always need to modify a larger value for token_max to resolve the issue.I hope to enhance the functionality by dynamically modifying the value of token_max through obtaining the model used by the current chain.
- Issue: The issue of encountering frequent errors with long documents when using the large tokens model has been addressed and fixed.
- Dependencies: We need a more dynamic way of obtaining the model name in order to retrieve the maximum tokens of the model. This aspect still requires further improvement.
- Tag maintainer: @hwchase17 @dev2049 @vowelparrot
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PR_kwDOIPDwls5T3WBm
| 6,734 |
feat: fix the Chinese characters in the solution content will be conv…
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[] | null | 1 | 2023-06-26T02:20:18 | 2023-06-26T20:14:49 | 2023-06-26T20:14:48 |
CONTRIBUTOR
| null |
fix the Chinese characters in the solution content will be converted to ascii encoding, resulting in an abnormally long number of tokens
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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| 1,773,678,248 |
I_kwDOIPDwls5puC6o
| 6,733 |
Issue: Continue from a saved checkpoint
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[] | null | 4 | 2023-06-26T02:17:33 | 2023-12-26T16:01:20 | null |
NONE
| null |
### Issue you'd like to raise.
I am looking for help to continue langchain Refine chain from a saved checkpoint. None of my code got completed because of GPT API overload. Any help would be appreciated.
### Suggestion:
_No response_
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PR_kwDOIPDwls5T3KTX
| 6,732 |
Throw error if trying to deserialize python prompts
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[] | null | 3 | 2023-06-26T01:19:26 | 2023-08-03T20:23:01 | 2023-08-03T20:23:01 |
CONTRIBUTOR
| null |
Removes the ability to deserialize Python prompts and throws an error informing of the security issues with a link to documentation on how to use JSON and YAML instead.
The docs don't even talk about Python, and the saving function in the base prompt class doesn't support saving to Python either. I'm guessing this is something left in for backwards compatibility, but I think due to the CVEs we should just remove it.
Fixes #6627
Fixes #4849
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I_kwDOIPDwls5ptp1S
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TypeError: __init__() takes exactly 1 positional argument (2 given)
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[] | null | 3 | 2023-06-25T23:58:49 | 2023-06-26T23:52:15 | 2023-06-26T23:52:15 |
NONE
| null |
### System Info
langchain:0.0.215
### 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
```
from langchain.indexes import VectorstoreIndexCreator
from langchain.embeddings import OpenAIEmbeddings
index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings('gpt2')).from_loaders([loader])
```
error:
```
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[6], line 3
1 from langchain.indexes import VectorstoreIndexCreator
2 from langchain.embeddings import OpenAIEmbeddings
----> 3 index = VectorstoreIndexCreator(embedding=OpenAIEmbeddings('gpt2')).from_loaders([loader])
File [~/micromamba/envs/openai/lib/python3.11/site-packages/pydantic/main.py:332], in pydantic.main.BaseModel.__init__()
TypeError: __init__() takes exactly 1 positional argument (2 given)
```
### Expected behavior
no
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PR_kwDOIPDwls5T29Fp
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Grobid parser for Scientific Articles from PDF
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CONTRIBUTOR
| null |
### Scientific Article PDF Parsing via Grobid
`Description:`
This change adds the GrobidParser class, which uses the Grobid library to parse scientific articles into a universal XML format containing the article title, references, sections, section text etc. The GrobidParser uses a local Grobid server to return PDFs document as XML and parses the XML to optionally produce documents of individual sentences or of whole paragraphs. Metadata includes the text, paragraph number, pdf relative bboxes, pages (text may overlap over two pages), section title (Introduction, Methodology etc), section_number (i.e 1.1, 2.3), the title of the paper and finally the file path.
Grobid parsing is useful beyond standard pdf parsing as it accurately outputs sections and paragraphs within them. This allows for post-fitering of results for specific sections i.e. limiting results to the methodology section or results. While sections are split via headings, ideally they could be classified specifically into introduction, methodology, results, discussion, conclusion. I'm currently experimenting with chatgpt-3.5 for this function, which could later be implemented as a textsplitter.
`Dependencies:`
For use, the grobid repo must be cloned and Java must be installed, for colab this is:
```
!apt-get install -y openjdk-11-jdk -q
!update-alternatives --set java /usr/lib/jvm/java-11-openjdk-amd64/bin/java
!git clone https://github.com/kermitt2/grobid.git
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-11-openjdk-amd64"
os.chdir('grobid')
!./gradlew clean install
```
Once installed the server is ran on localhost:8070 via
```
get_ipython().system_raw('nohup ./gradlew run > grobid.log 2>&1 &')
```
@rlancemartin, @eyurtsev
Twitter Handle: @Corranmac
Grobid Demo Notebook is [here](https://colab.research.google.com/drive/1X-St_mQRmmm8YWtct_tcJNtoktbdGBmd?usp=sharing).
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Zep Authentication
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CONTRIBUTOR
| null |
## Description: Add Zep API Key argument to ZepChatMessageHistory and ZepRetriever
- correct docs site links
- add zep api_key auth to constructors
ZepChatMessageHistory: @hwchase17,
ZepRetriever: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5T26dG
| 6,727 |
prevent DuckDuckGoSearchAPIWrapper from consuming top result
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[] | null | 2 | 2023-06-25T23:14:50 | 2023-06-26T02:54:16 | 2023-06-26T02:54:16 |
CONTRIBUTOR
| null |
remove the `next` call that checks for None on the results generator
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: remove the `next` call that checks for None on the results generator
- Issue: #6724 ,
- Dependencies: none,
- Tag maintainer: @vowelparrot,
- Twitter handle: gabrielaltay
-
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5ptj66
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ValueError: Unknown encoding text-embedding-ada-002
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[] | null | 5 | 2023-06-25T23:08:34 | 2023-12-19T12:49:06 | 2023-10-05T16:08:36 |
NONE
| null |
### System Info
no
### Who can help?
@hwchase17 @agola11
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [x] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
when running the following code:
```
from langchain.embeddings import OpenAIEmbeddings
embedding_model = OpenAIEmbeddings()
embeddings = embedding_model.embed_documents(
[
"Hi there!",
"Oh, hello!",
"What's your name?",
"My friends call me World",
"Hello World!"
]
)
```
such errors occurred:
```
ValueError Traceback (most recent call last)
Cell In[9], line 1
----> 1 embeddings = embedding_model.embed_documents(
2 [
3 "Hi there!",
4 "Oh, hello!",
5 "What's your name?",
6 "My friends call me World",
7 "Hello World!"
8 ]
9 )
10 len(embeddings), len(embeddings[0])
File ~/micromamba/envs/openai/lib/python3.11/site-packages/langchain/embeddings/openai.py:305, in OpenAIEmbeddings.embed_documents(self, texts, chunk_size)
293 """Call out to OpenAI's embedding endpoint for embedding search docs.
294
295 Args:
(...)
301 List of embeddings, one for each text.
302 """
303 # NOTE: to keep things simple, we assume the list may contain texts longer
304 # than the maximum context and use length-safe embedding function.
--> 305 return self._get_len_safe_embeddings(texts, engine=self.deployment)
File ~/micromamba/envs/openai/lib/python3.11/site-packages/langchain/embeddings/openai.py:225, in OpenAIEmbeddings._get_len_safe_embeddings(self, texts, engine, chunk_size)
223 tokens = []
224 indices = []
--> 225 encoding = tiktoken.get_encoding(self.model)
226 for i, text in enumerate(texts):
227 if self.model.endswith("001"):
228 # See: https://github.com/openai/openai-python/issues/418#issuecomment-1525939500
229 # replace newlines, which can negatively affect performance.
File ~/micromamba/envs/openai/lib/python3.11/site-packages/tiktoken/registry.py:60, in get_encoding(encoding_name)
57 assert ENCODING_CONSTRUCTORS is not None
59 if encoding_name not in ENCODING_CONSTRUCTORS:
---> 60 raise ValueError(f"Unknown encoding {encoding_name}")
62 constructor = ENCODING_CONSTRUCTORS[encoding_name]
63 enc = Encoding(**constructor())
ValueError: Unknown encoding text-embedding-ada-002
```
how to fix it?
### Expected behavior
no
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[] | null | 1 | 2023-06-25T23:02:12 | 2023-06-25T23:03:01 | 2023-06-25T23:03:00 |
CONTRIBUTOR
| null |
- 2markdown loader (#4796)
- Add dev option (#4828)
- Add Support for Flexible Input Format for LLM and Chat Model Runs (#4805)
- Typos (#4851)
- Remove unnecessary comment (#4845)
- bump 172 (#4864)
- Hugging Face Loader: Add lazy load (#4799)
- Fix TypeError in Vectorstore Redis class methods (#4857)
- Add beautiful soup 4 to extended testing extra (#4869)
- Remove unused variables in Milvus vectorstore (#4868)
- Update getting_started.ipynb (#4850)
- docs `retriever` improvements (#4430)
- fix homepage typo (#4883)
- Tiny code review and docs fix for Docugami DataLoader (#4877)
- feat(Add FastAPI + Vercel deployment option): (#4520)
- Bold Crumbs (#4876)
- ConversationalChatAgent: Allow customizing `TEMPLATE_TOOL_RESPONSE` (#2361)
- Faiss no avx2 (#4895)
- Add a generic document loader (#4875)
- Add html parsers (#4874)
- Cadlabs/python tool sanitization (#4754)
- Zep memory (#4898)
- Update gallery (#4873)
- Fix AzureOpenAI embeddings documentation example. model -> deployment (#4389)
- Update getting_started.md (#4482)
- docs: text splitters improvements (#4490)
- Harrison/serper api bug (#4902)
- Harrison/faiss norm (#4903)
- Harrison/improved retry tool (#4842)
- Harrison/unified objectives (#4905)
- bump version to 173 (#4910)
- Load specific file types from Google Drive (issue #4878) (#4926)
- API update: Engines -> Models (#4915)
- feat #4479: TextLoader auto detect encoding and improved exceptions (#4927)
- Fix bilibili (#4860)
- Add human message as input variable to chat agent prompt creation (#4542)
- add alias for model (#4553)
- dont error on sql import (#4647)
- docs: compound ecosystem and integrations (#4870)
- Update GPT4ALL integration (#4567)
- FIX: GPTCache cache_obj creation loop (#4827)
- Redis kwargs fix (#4936)
- Update redis integration tests (#4937)
- docs supabase update (#4935)
- Correct typo in APIChain example notebook (Farenheit -> Fahrenheit) (#4938)
- fix: error in gptcache example nb (#4930)
- Update custom_multi_action_agent.ipynb (#4931)
- docs: added `ecosystem/dependents` page (#4941)
- docs: vectorstores, different updates and fixes (#4939)
- Chatconv agent: output parser exception (#4923)
- Zep Retriever - Vector Search Over Chat History (#4533)
- Fix get_num_tokens for Anthropic models (#4911)
- NIT: Instead of hardcoding k in each definition, define it as a param above. (#2675)
- [nit] Simplify Spark Creation Validation Check A Little Bit (#4761)
- Fix for syntax when setting search_path for Snowflake database (#4747)
- Harrison/spell executor (#4914)
- Add Spark SQL support (#4602) (#4956)
- Support Databricks in SQLDatabase (#4702)
- Fixed assumptions misspelling (#4961)
- Update tutorials.md (#4960)
- Update planner_prompt.py (#4967)
- power bi api wrapper integration tests & bug fix (#4983)
- bump v0.0.174 (#4988)
- Remove autoreload in examples (#4994)
- Bug fixes and error handling in Redis - Vectorstore (#4932)
- Add async search with relevance score (#4558)
- Make test gha workflow manually runnable (#4998)
- Adds 'IN' metadata filter for pgvector for checking set presence (#4982)
- Update python.py (#4971)
- PGVector logger message level (#4920)
- feature/4493 Improve Evernote Document Loader (#4577)
- Fix graphql tool (#4984)
- changed ValueError to ImportError (#5006)
- docs: Big Mendable Improvements (#4964)
- added instruction about pip install google-gerativeai (#5004)
- Update the GPTCache example (#4985)
- Revert "API update: Engines -> Models (#4915)" (#5008)
- Add self query translator for weaviate vectorstore (#4804)
- Check for single prompt in __call__ method of the BaseLLM class (#4892)
- Add logs command (#5007)
- fix prompt saving (#4987)
- Streaming only final output of agent (#2483) (#4630)
- bump v175 (#5041)
- Fix annoying typo in docs (#5029)
- Add documentation for Databricks integration (#5013)
- DOC: Misspelling in agents.rst documentation (#5038)
- change to type checking (#5062)
- Harrison/psychic (#5063)
- bump to 176 (#5064)
- move docs
- feat: batch multiple files in a single Unstructured API request (#4525)
- preserve language in conversation retrieval (#4969)
- docs: `Deployments` page moved into `Ecosystem/` (#4949)
- Separate Runner Functions from Client (#5079)
- Add 'get_token_ids' method (#4784)
- Improved query, print & exception handling in REPL Tool (#4997)
- Harrison/neo4j (#5078)
- Bump 177 (#5095)
- fix: revert docarray explicit transitive dependencies and use extras instead (#5015)
- Improving Resilience of MRKL Agent (#5014)
- Improve pinecone hybrid search retriever adding metadata support (#5098)
- Add the usage of SSL certificates for Elasticsearch and user password authentication (#5058)
- add get_top_k_cosine_similarity method to get max top k score and index (#5059)
- PowerBI major refinement in working of tool and tweaks in the rest (#5090)
- fix: add_texts method of Weaviate vector store creats wrong embeddings (#4933)
- update langchainplus client and docker file to reflect port changes (#5005)
- Fixed import error for AutoGPT e.g. from langchain.experimental.auton… (#5101)
- Update serpapi.py (#4947)
- changed ValueError to ImportError (#5103)
- fix: assign current_time to datetime.now() if current_time is None (#5045)
- Add Mastodon toots loader (#5036)
- Add OpenLM LLM multi-provider (#4993)
- Pass Dataset Name by Name not Position (#5108)
- Fixes issue #5072 - adds additional support to Weaviate (#5085)
- Improve effeciency of TextSplitter.split_documents, iterate once (#5111)
- WhyLabs callback (#4906)
- Add AzureCognitiveServicesToolkit to call Azure Cognitive Services API (#5012)
- Add link to Psychic from document loaders documentation page (#5115)
- bump 178 (#5130)
- docs: fix minor typo + add wikipedia package installation part in human_input_llm.ipynb (#5118)
- solving #2887 (#5127)
- Improve PlanningOutputParser whitespace handling (#5143)
- Add ElasticsearchEmbeddings class for generating embeddings using Elasticsearch models (#3401)
- Adding Weather Loader (#5056)
- Add MosaicML inference endpoints (#4607)
- Empty check before pop (#4929)
- Add async versions of predict() and predict_messages() (#4867)
- fix: fix current_time=Now bug for aadd_documents in TimeWeightedRetriever (#5155)
- Docs: updated getting_started.md (#5151)
- Clarification of the reference to the "get_text_legth" function in ge… (#5154)
- docs: added missed `document_loaders` examples (#5150)
- Add Typesense vector store (#1674)
- Vectara (#5069)
- Beam (#4996)
- Update rellm_experimental.ipynb (#5189)
- example usage (#5182)
- adjust docarray docstrings (#5185)
- bump 179 (#5200)
- Harrison/modelscope (#5156)
- Reuse `length_func` in `MapReduceDocumentsChain` (#5181)
- Update Cypher QA prompt (#5173)
- Improve weaviate vectorstore docs (#5201)
- tfidf retriever (#5114)
- standardize json parsing (#5168)
- fixing total cost finetuned model giving zero (#5144)
- Fixes scope of query Session in PGVector (#5194)
- Output parsing variation allowance (#5178)
- Allow readthedoc loader to pass custom html tag (#5175)
- Add Iugu document loader (#5162)
- Add Joplin document loader (#5153)
- nit (#5208)
- add option to pass openai key to langchain plus command (#5213)
- Log warning (#5192)
- Add Delete Session Method (#5193)
- Add 'status' command to get server status (#5197)
- Harrison/vertex (#5049)
- fix a mistake in concepts.md (#5222)
- Create async copy of from_text() inside GraphIndexCreator. (#5214)
- Remove API key from docs (#5223)
- Change Default GoogleDriveLoader Behavior to not Load Trashed Files (issue #5104) (#5220)
- Allow to specify ID when adding to the FAISS vectorstore. (#5190)
- Bibtex integration for document loader and retriever (#5137)
- Add MiniMax embeddings (#5174)
- Weaviate: Add QnA with sources example (#5247)
- Fix typo in docstring of RetryWithErrorOutputParser (#5244)
- bump 180 (#5248)
- remove extra "\n" to ensure that the format of the description, examp… (#5232)
- Resolve error in StructuredOutputParser docs (#5240)
- Added the option of specifying a proxy for the OpenAI API (#5246)
- OpenSearch top k parameter fix (#5216)
- Fixed regression in JoplinLoader's get note url (#5265)
- Docs link custom agent page in getting started (#5250)
- Zep sdk version (#5267)
- Add C Transformers for GGML Models (#5218)
- Add visible_only and strict_mode options to ClickTool (#4088)
- Add Multi-CSV/DF support in CSV and DataFrame Toolkits (#5009)
- OpenAI lint (#5273)
- Added pipline args to `HuggingFacePipeline.from_model_id` (#5268)
- Support bigquery dialect - SQL (#5261)
- feat: add Momento as a standard cache and chat message history provider (#5221)
- Fixed typo: 'ouput' to 'output' in all documentation (#5272)
- Tedma4/twilio tool (#5136)
- LLM wrapper for Databricks (#5142)
- Add an example to make the prompt more robust (#5291)
- Update CONTRIBUTION guidelines and PR Template (#5140)
- Fixed passing creds to VertexAI LLM (#5297)
- bump 181 (#5302)
- Better docs for weaviate hybrid search (#5290)
- Add instructions to pyproject.toml (#5138)
- docs: improve flow of llm caching notebook (#5309)
- Fix typos (#5323)
- docs: added link to LangChain Handbook (#5311)
- add enum output parser (#5165)
- add enum output parser (#5165)
- Add Chainlit to deployment options (#5314)
- Fixing blank thoughts in verbose for "_Exception" Action (#5331)
- fix: remove empty lines that cause InvalidRequestError (#5320)
- Sample Notebook for DynamoDB Chat Message History (#5351)
- added cosmos kwargs option (#5292)
- feat: support for shopping search in SerpApi (#5259)
- Add SKLearnVectorStore (#5305)
- bump 182 (#5364)
- Fixes iter error in FAISS add_embeddings call (#5367)
- revert bad json (#5370)
- bump to 183 (#5372)
- Add path validation to DirectoryLoader (#5327)
- Fix: Handle empty documents in ContextualCompressionRetriever (Issue #5304) (#5306)
- handle json parsing errors (#5371)
- Use Default Factory (#5380)
- Update PR template with Twitter handle request (#5382)
- fix: Blob.from_data mimetype is lost (#5395)
- Add async support to routing chains (#5373)
- Fix update_document function, add test and documentation. (#5359)
- Update llamacpp demonstration notebook (#5344)
- Removed deprecated llm attribute for load_chain (#5343)
- Harrison/llamacpp (#5402)
- Add pagination for Vertex AI embeddings (#5325)
- Reformat openai proxy setting as code (#5330)
- Harrison/deep infra (#5403)
- Harrison/prediction guard update (#5404)
- Implemented appending arbitrary messages (#5293)
- docs: `ecosystem/integrations` update 2 (#5282)
- docs: `ecosystem/integrations` update 1 (#5219)
- Harrison/datetime parser (#4693)
- bump version 184 (#5407)
- Add ToolException that a tool can throw. (#5050)
- Harrison/text splitter (#5417)
- New Trello document loader (#4767)
- DocumentLoader for GitHub (#5408)
- Harrison/spark reader (#5405)
- Set old LCTracer to default to port 8000 (#5381)
- Rename and fix typo in lancedb (#5425)
- Harrison/condense q llm (#5438)
- adding MongoDBAtlasVectorSearch (#5338)
- Add more code splitters (go, rst, js, java, cpp, scala, ruby, php, swift, rust) (#5171)
- bump 185 (#5442)
- fix (#5457)
- bump 186 (#5459)
- Fixed docstring in faiss.py for load_local (#5440)
- Removes duplicated call from langchain/client/langchain.py (#5449)
- `encoding_kwargs` for InstructEmbeddings (#5450)
- MRKL output parser no longer breaks well formed queries (#5432)
- docs: cleaning (#5413)
- Added async _acall to FakeListLLM (#5439)
- Feat: Add batching to Qdrant (#5443)
- Update psychicapi version (#5471)
- Add maximal relevance search to SKLearnVectorStore (#5430)
- add simple test for imports (#5461)
- Ability to specify credentials wihen using Google BigQuery as a data loader (#5466)
- convert the parameter 'text' to uppercase in the function 'parse' of the class BooleanOutputParser (#5397)
- added n_threads functionality for gpt4all (#5427)
- Allow for async use of SelfAskWithSearchChain (#5394)
- Allow ElasticsearchEmbeddings to create a connection with ES Client object (#5321)
- SQLite-backed Entity Memory (#5129)
- py tracer fixes (#5377)
- Harrison/html splitter (#5468)
- Feature: Qdrant filters supports (#5446)
- Add matching engine vectorstore (#3350)
- code splitter docs (#5480)
- Bedrock llm and embeddings (#5464)
- add more vars to text splitter (#5503)
- bump 187 (#5504)
- Add Feedback Methods + Evaluation examples (#5166)
- Add allow_download as attribute for GPT4All (#5512)
- added DeepLearing.AI course link (#5518)
- Skips creating boto client for Bedrock if passed in constructor (#5523)
- Add Managed Motorhead (#5507)
- Replace enumerate with zip. (#5527)
- Add minor fixes for PySpark Document Loader Docs (#5525)
- docs: unstructured no longer requires installing detectron2 from source (#5524)
- Replace list comprehension with generator. (#5526)
- Add param requests_kwargs for WebBaseLoader (#5485)
- Replace loop appends with list comprehension. (#5528)
- [retrievers][knn] Replace loop appends with list comprehension. (#5529)
- Fix wrong class instantiation in docs MMR example (#5501)
- Add texts with embeddings to PGVector wrapper (#5500)
- make BaseEntityStore inherit from BaseModel (#5478)
- docs `ecosystem/integrations` update 3 (#5470)
- feat(integrations): Add WandbTracer (#4521)
- add maxcompute (#5533)
- add brave search util (#5538)
- Corrects inconsistently misspelled variable name. (#5559)
- Qdrant self query (#5567)
- bump 188 (#5568)
- make the elasticsearch api support version which below 8.x (#5495)
- Fix typo in docugami.ipynb (#5571)
- Add missing comma in conv chat agent prompt json (#5573)
- Fix bedrock auth validation (#5574)
- Documentation fixes (linting and broken links) (#5563)
- nit (#5578)
- Fix SQLAlchemy truncating text when it is too big (#5206)
- docs(integration): update colab and external links in WandbTracing docs (#5602)
- Rm Template Title (#5616)
- human approval callback (#5581)
- Es knn index search 5346 (#5569)
- Fix: Qdrant ids (#5515)
- Dev2049/add argilla callback (#5621)
- bump 189 (#5620)
- fix chroma update_document to embed entire documents, fixes a characer-wise embedding bug (#5584)
- Fix bedrock llm boto3 client instantiation (#5629)
- Update Tracer Auth / Reduce Num Calls (#5517)
- fix import issue (#5636)
- feat: add `UnstructuredExcelLoader` for `.xlsx` and `.xls` files (#5617)
- Fixed multi input prompt for MapReduceChain (#4979)
- docs: `modules` pages simplified (#5116)
- minor refactor GenerativeAgentMemory (#5315)
- pref: reduce DB query error rate (#5339)
- Update confluence.py to return spaces between elements (#5383)
- Implements support for Personal Access Token Authentication in the ConfluenceLoader (#5385)
- Adds the option to pass the original prompt into the AgentExecutor for PlanAndExecute agents (#5401)
- QuickFix for FinalStreamingStdOutCallbackHandler: Ignore new lines & white spaces (#5497)
- Add parameters to send_message() call for vertexai chat models (PaLM2) (#5566)
- handle single arg to and/or (#5637)
- docs `ecosystem/integrations` update 4 (#5590)
- nit: pgvector python example notebook, fix variable reference (#5595)
- Harrison/update azure nb (#5665)
- Harrison/pubmed integration (#5664)
- removing client+namespace in favor of collection (#5610)
- fix: correct momento chat history notebook typo and title (#5646)
- Created fix for 5475 (#5659)
- FileCallbackHandler (#5589)
- refactor: BaseStringMessagePromptTemplate from_template method (#5332)
- Update youtube.py - Fix metadata validation error in YoutubeLoader (#5479)
- Improve Error Messaging for APOC Procedure Failure in Neo4jGraph (#5547)
- Support similarity_score_threshold retrieval with Chroma (#5655)
- This fixes issue #5651 - GPT4All wrapper loading issue (#5657)
- Add args_schema to google_places tool (#5680)
- Harrison/pipeline prompt (#5540)
- Added Dependencies Status, Open issues and releases badges in Readme.md (#5681)
- SQL agent : Improved prompt engineering prevents agent guessing database column names. (#5671)
- Retitles Bedrock doc to appear in correct alphabetical order in site nav (#5639)
- Update MongoDBChatMessageHistory to create an index on SessionId (#5632)
- Raise an exception in MKRL and Chat Output Parsers if parsing text which contains both an action and a final answer (#5609)
- refactor: extract token text splitter function (#5179)
- sqlalchemy MovedIn20Warning declarative_base DEPRICATION fix (#5676)
- top_k and top_p transposed in vertexai (#5673)
- bump version to 190 (#5704)
- Addresses GPT4All wrapper model_type attribute issues #5720. (#5743)
- docs: Added Deploying LLMs into production + a new ecosystem (#4047)
- Adding support to save multiple memories at a time. Cuts save time by … (#5172)
- Cypher search: Check if generated Cypher is provided in backticks (#5541)
- Zep Hybrid Search (#5742)
- Removes unnecessary backslash escaping for backticks in python (#5751)
- Fix a typo in the documentation for the Slack document loader (#5745)
- Error in documentation: Chroma constructor (#5731)
- Integrate Clickhouse as Vector Store (#5650)
- Create OpenAIWhisperParser for generating Documents from audio files (#5580)
- docs: `ecosystem/integrations` update 5 (#5752)
- docs: updated `ecosystem/dependents` (#5753)
- Add class attribute "return_generated_question" to class "BaseConversationalRetrievalChain" (#5749)
- Add aviary support (#5661)
- cohere retries (#5757)
- Strips whitespace and \n from loc before filtering urls from sitemap (#5728)
- Harrison/youtube multi language (#5758)
- fix markdown text splitter horizontal lines (#5625)
- Tracing Group (#5326)
- Update tutorials.md (#5761)
- feat: Support for `Tigris` Vector Database for vector search (#5703)
- Scores are explained in vectorestore docs (#5613)
- bump ver 191 (#5766)
- Use client from LCP-SDK (#5695)
- fix ver 191 (#5784)
- Set Falsey (#5783)
- Attribute support for html tags (#5782)
- support returning run info for llms, chat models and chains (#5666)
- fixing from_documents method of the MongoDB Atlas vector store (#5794)
- Revise DATABRICKS_API_TOKEN as DATABRICKS_TOKEN (#5796)
- YoutubeAudioLoader and updates to OpenAIWhisperParser (#5772)
- Base RunEvaluator Chain (#5750)
- [Docs][Hotfix] Fix broken links (#5800)
- Add UTF-8 json ouput support while langchain.debug is set to True. (#5802)
- WIP: openai settings (#5792)
- added support for different types in ResponseSchema class (#5789)
- fixed faiss integ tests (#5808)
- add doc about reusing MongoDBAtlasVectorSearch (#5805)
- Update adding_memory.ipynb (#5806)
- rm docs mongo (#5811)
- bump ver to 192 (#5812)
- fix: fullfill openai params when embedding (#5821)
- bump version to 193 (#5838)
- Add in the async methods and link the run id (#5810)
- feat: issue-5712 add sleep tool (#5715)
- Rm extraneous args to the trace group helper (#5801)
- FIX: backslash escaped (#5815)
- Add DeepInfra embeddings integration with tests and examples, better exception handling for Deep Infra LLM (#5854)
- Add how to use a custom scraping function with the sitemap loader. (#5847)
- feat: Add `UnstructuredCSVLoader` for CSV files (#5844)
- Add logging in PBI tool (#5841)
- Add additional VertexAI Params (#5837)
- Update microsoft loader example with docx2txt dependency (#5832)
- docs: add Shale Protocol integration guide (#5814)
- qdrant vector store - search with relevancy scores (#5781)
- Update to Getting Started docs page for Memory (#5855)
- Fix exporting GCP Vertex Matching Engine from vectorstores (#5793)
- Add knn and query search field options to ElasticKnnSearch (#5641)
- propagate callbacks to ConversationalRetrievalChain (#5572)
- Fix serialization issue with W&B (#5693)
- Added SingleStoreDB Vector Store (#5619)
- Refactor and update databricks integration page (#5575)
- Implement saving and loading of RetrievalQA chain (#5818)
- Harrison/fauna loader (#5864)
- Harrison/nebula graph (#5865)
- bump version to 194 (#5866)
- fix: update qa_chain doc for "chai_type" (#5877)
- Update run eval imports in init (#5858)
- Fix the shortcut conflict for document page search (#5874)
- Fixes model arguments for amazon models (#5896)
- Feature/add AWS Kendra Index Retriever (#5856)
- Use LCP Client in Tracer (#5908)
- Create snowflake Loader (#5825)
- arxiv: Correct name of search client attribute to 'arxiv_search' from incorrect 'arxiv_client' (#5917)
- Fix typo (#5894)
- Baseten integration (#5862)
- Add start index to metadata in TextSplitter (#5912)
- Fix openai proxy error (#5914)
- Fix the issue where the parameters passed to VertexAI ignored #5889 (#5891)
- Add support for the endpoint URL in DynamoDBChatMesasgeHistory (#5836)
- Expose full params in Qdrant (#5947)
- fixes to docs (#5919)
- bump ver to 195 (#5949)
- Add load() to snowflake loader (#5956)
- LOTR: Lord of the Retrievers. A retriever that merge several retrievers together applying document_formatters to them. (#5798)
- bump version to 196 (#5988)
- Fix: SnowflakeLoader returning empty documents (#5967)
- Fixed typo missing "use" (#5991)
- add test for structured tools (#5989)
- Fix handling of missing action & input for async MRKL agent (#5985)
- Add additional parameters to Graph Cypher Chain (#5979)
- Add a new vector store - AwaDB (#5971) (#5992)
- Create Airtable loader (#5958)
- feat: Add `UnstructuredXMLLoader` for `.xml` files (#5955)
- Update to Vectara integration (#5950)
- fix: use model token limit not tokenizer ditto (#5939)
- Fix: Grammer fix in documentation (#5925)
- Fix IndexError in RecursiveCharacterTextSplitter (#5902)
- add ocr_languages param for ConfluenceLoader.load() (#5823)
- Harrison/arxiv fix (#5993)
- Obey handler.raise_error in _ahandle_event_for_handler (#6001)
- support kwargs (#5990)
- bump version to 197 (#6007)
- Update databricks.md (#6006)
- [APIChain] enhance the robustness or url (#6008)
- feat: :sparkles: Added filtering option to FAISS vectorstore (#5966)
- fix: TypeError when loading confluence pages by cql (#5878)
- Harrison/embaas (#6010)
- I before E (#6015)
- nc/load (#5733)
- Update check (#6020)
- Langchain decorators (#6017)
- Remove from PR template (#6018)
- add from_documents interface in awadb vector store (#6023)
- Harrison/hologres (#6012)
- Update MongoDB Atlas support docs (#6022)
- add dashscope text embedding (#5929)
- Harrison/cognitive search (#6011)
- bump ver to 198 (#6026)
- Mongo db doc fix (#6042)
- embaas title
- comment out
- chore: spedd up integration test by using smaller model (#6044)
- Text splitter for Markdown files by header (#5860)
- Log tracer errors (#6066)
- Add embaas document extraction api endpoints (#6048)
- improve tools (#6062)
- propogate kwargs fully (#6076)
- Enable serialization for anthropic (#6049)
- turn off repr (#6078)
- Use Run object from SDK (#6067)
- Fix for ModuleNotFoundError while running langchain-server. Issue #5833 (#6077)
- Add tests and update notebook for MarkdownHeaderTextSplitter (#6069)
- support functions (#6099)
- convert tools to openai (#6100)
- Implement `max_marginal_relevance_search` in `VectorStore` of Pinecone (#6056)
- bump version to 199 (#6102)
- Harrison/notebook functions (#6103)
- Add support for tags (#5898)
- support streaming for functions (#6115)
- Return session name in runner response (#6112)
- add functions agent (#6113)
- bump ver to 200 (#6130)
- Add Run Collector Callback (#6133)
- Update Name (#6136)
- Update readthedocs_documentation.ipynb (#6148)
- typo: 'following following' to 'following' (#6163)
- Add docs for tags (#6155)
- feat: Add support for the Solidity language (#6054)
- feat: add content_format param to ConfluenceLoader.load() (#5922)
- count tokens for new OpenAI model versions (#6195)
- Fix typo `pandocs` to `pandoc` (#6203)
- Harrison/use functions agent (#6185)
- bump version to 201 (#6233)
- Add Tags for LLMs (#6229)
- Feature/add acreom loader (#5780)
- Support chat history persistence in AutoGPT (#5716)
- Improve Error Message for failed callback (#6247)
- Harrison/openai functions (#6223)
- Harrison/openai functions (#6261)
- Fix: change the chatgpt plugin retriever metadata format (#5920)
- Update output format of MosaicML endpoint to be more flexible (#6060)
- ArxivAPIWrapper - doc_content_chars_max (#6063)
- bump to 202 (#6262)
- Doc refactor (#6300)
- rm ignore_vercel (#6302)
- update api link (#6303)
- update readme (#6304)
- basic redirect (#6309)
- more redirect (#6314)
- Update dev container (#6189)
- Del linkcheck readme (#6317)
- Fixes #6282 (#6283)
- nit (#6305)
- Improve the performance of add_texts interface and upgrade the AwaDB from 0.3.2 to 0.3.3 (#6316)
- fix eval guide links (#6319)
- Harrison/deeplake new features (#6263)
- Better Entity Memory code documentation (#6318)
- Handle Managed Motorhead Data Key (#6169)
- changed height in the nb example (#6327)
- fix links to prompt templates and example selectors (#6332)
- DocArray as a Retriever (#6031)
- make modelname_to_contextsize as a staticmethod (#6040)
- Improve AnalyticDB Vector Store implementation without affecting user (#6086)
- Add oobabooga/text-generation-webui support as a llm (#5997)
- Allow GoogleDrive to authenticate via application default credentials on Cloud Run/GCE etc without service key (#6035)
- qdrant: search by vector (#6043)
- Harrison/error zero tools (#6340)
- Harrison/faiss score (#6341)
- Custom Anthropic API URL (#6221)
- fix spelling
- Make lckwargs private (#6344)
- fix titles in documentation
- update web_base.py to have verify option (#6107)
- Add new openai 0613 model costs (#6110)
- Add ignore vercel preview script (#6320)
- Harrison/mmr support for opensearch (#6349)
- Update MD header text splitter notebook (#6339)
- Add self query retriever example with MD header splitting (#6359)
- bump 203 (#6372)
- Guardrails output parser: Pass LLM api for reasking (#6089)
- Harrison/gdrive enhancements (#6375)
- Extend `ArgillaCallbackHandler` support (#6153)
- Add the ability to run the map_reduce chains process results step as async (#6181)
- fix link of callbacks on modules page (#6323)
- Fixed PermissionError on windows (#6170)
- searx - docs
- Harrison/myscale self query (#6376)
- Harrison/metaphor search fix (#6387)
- Harrison/zep mem (#6388)
- Fix class promotion (#6187)
- Harrison/functions docs improvements (#6389)
- Add option to save/load graph cypher QA (#6219)
- Fix output final text for HuggingFaceTextGenInference when streaming (#6211)
- OpenAI functions dont work with async streaming... #6225 (#6226)
- searx_search: updated tools and doc (#6276)
- Fix integration tests for Faiss vector store (#6281)
- [hotfix] Deep Lake fails on newer version due to hardcode (#6383)
- Fix latest clickhouse vector schema change (#6385)
- Fix typo in the CAI critique prompt (#6123)
- Correct AzureSearch Vector Store not applying search_kwargs when searching (#6132)
- Fixed an unhandled error that was raised when DynamoDB did not have any chat history. (#6141)
- Fix LLM types so that they can be loaded from config dicts (#6235)
- add max_context_size property in BaseOpenAI (#6239)
- Add markdown to specify important arguments (#6246)
- Update web_base.ipynb for guiding purposes (#6248)
- Iterate through filtered file types instead of all listed files (#6258)
- Fixes typo in Vectara.similarity_search (#6277)
- - Fix pass system_message argument in new feature openai_functions_agent (#6297)
- Added gpt-3.5-turbo 0613 16k and 16k-0613 pricing (#6287)
- rm pandas from arize (#6392)
- Harrison/typesense fix (#6391)
- Harrison/chroma fix (#6390)
- Update web_base.py _fetch() method For SiteMapLoader (#6256)
- support memory for functions (#6165)
- fix prod docs build (#6402)
- Docs nit (#6350)
- changes to llm chain (#6328)
- Harrison/refactor functions (#6408)
- bump version to 205 (#6410)
- Include placeholder value for all secrets, not just kwargs (#6421)
- remove mongo
- expose docs chains (#6453)
- Run eval in eval mode (#6447)
- Update arize_callback.py (#6433)
- Add Trajectory Eval RunEvaluator (#6449)
- fix anthropic chat model mutating input list (#6457)
- Remove extra word in the introduction documentation (#6450)
- Add example for question answering over documents with OpenAI Function Agent (#6448)
- Add `_similarity_search_with_relevance_scores` in `Pinecone` (#6446)
- Update web_base.ipynb (#6430)
- Remove backticks without clear purpose from docs (#6442)
- Fix Custom LLM Agent example (#6429)
- Update introduction.mdx (#6425)
- docs `retrievers` fixes (#6299)
- Fixed a link typo /-/route -> /-/routes. and change endpoint format (#6186)
- Incorrect argument count handling (#5543)
- Harrison/functions in retrieval (#6463)
- Fix for #6431 - chatprompt template with partial variables giing validation error (#6456)
- Update SinglStoreDB vectorstore (#6423)
- Fix broken links in autonomous agents docs (#6398)
- Fix the issue where ANTHROPIC_API_URL set in environment is not takin… (#6400)
- Improve error message (#6275)
- Harrison/unstructured page number (#6464)
- feat: use latest duckduckgo_search API to call (#6409)
- fix: llm caching for replicate (#6396)
- Update serpapi.py Support baidu list type answer_box (#6386)
- fix neo4j schema query (#6381)
- bump version to 206 (#6465)
- add FunctionMessage support to `_convert_dict_to_message()` in OpenAI chat model (#6382)
- fix openai qa chain (#6487)
- Add Alibaba Cloud OpenSearch as a new vector store (#6154)
- release 207 (#6488)
- fix: change ddg to DDGS (#6480)
- improve documentation on base chain (#6468)
- Vector store support for Cassandra (#6426)
- Update notebook for MD header splitter and create new cookbook (#6399)
- docs/fix links (#6498)
- Fix link (#6501)
- Documentation Fix: Correct the example code output in the prompt templates doc (#6496)
- Fixed: 'readible' -> readable (#6492)
- Make streamlit import optional (#6510)
- Fix typo in docstring of format_tool_to_openai_function (#6479)
- typo(llamacpp.ipynb): 'condiser' -> 'consider' (#6474)
- Export trajectory eval fn (#6509)
- Update index.mdx (#6326)
- Add KuzuQAChain (#6454)
- Be able to use Codey models on Vertex AI (#6354)
- Add async support for HuggingFaceTextGenInference (#6507)
- Feat: Add a prompt template parameter to qa with structure chains (#6495)
- Integrate Rockset as Vectorstore (#6216)
- Fix issue with non-list `To` header in GmailSendMessage Tool (#6242)
- Update model token mappings/cost to include 0613 models (#6122)
- Infino integration for simplified logs, metrics & search across LLM data & token usage (#6218)
- Harrison/multi tool (#6518)
- bump to ver 208 (#6540)
- Relax string input mapper check (#6544)
- [Feature][VectorStore] Support StarRocks as vector db (#6119)
- Minor Grammar Fixes in Docs and Comments (#6536)
- Remove unintended double negation in docstring (#6541)
- update pr tmpl (#6552)
- feat: faiss filter from list (#6537)
- Wait for all futures (#6554)
- Fix whatsappchatloader - enable parsing new datetime format on WhatsApp chat (#6555)
- Remove duplicate databricks entries in ecosystem integrations (#6569)
- Change Data Loader Namespace (#6568)
- Detailed using the Twilio tool to send messages with 3rd party apps incl. WhatsApp (#6562)
- add motherduck docs (#6572)
- Upgrade the version of AwaDB and add some new interfaces (#6565)
- feat: interfaces for async embeddings, implement async openai (#6563)
- Add OpenLLM wrapper(#6578)
- Add AzureML endpoint LLM wrapper (#6580)
- Add missing word in comment (#6587)
- Clarifai integration (#5954)
- bump 209 (#6593)
- Fix callback forwarding in async plan method for OpenAI function agent (#6584)
- MD header text splitter returns Documents (#6571)
- add mongo (HOLD) (#6437)
- Allow callback handlers to opt into being run inline (#6424)
- StreamlitCallbackHandler (#6315)
- added redis method to delete entries by keys (#6222)
- Loader for OpenCityData and minor cleanups to Pandas, Airtable loaders (#6301)
- Add tags in agent initialization (#6559)
- Session to project (#6249)
- fix: remove callbacks arg from Tool and StructuredTool inferred schema (#6483)
- bump 210 (#6656)
- Openapi to openai (#6658)
- bump 211 (#6660)
- openapi_openai docstring (#6661)
- Create merge loader that combines documents from a set of loaders (#6659)
- Add delete and ensure add_texts performs upsert (w/ ID optional) (#6126)
- Recursive URL loader (#6455)
- ChatVertexAI broken - Fix error with sending context in params (#6652)
- Dev2049/bump 212 (#6665)
- fix(docs): broken link for OpenLLM (#6622)
- Fix grammar mistake in base.py in planners (#6611)
- Fix ray-project/Aviary integration (#6607)
- Fix Typo (#6595)
- Fix typo in myscale_self_query.ipynb (#6601)
- fix minor typo in vector_db_qa.mdx (#6604)
- Kendra retriever api (#6616)
- PowerBI: catch outdated token (#6634)
- update chroma notebook (#6664)
- openapi -> openai nit (#6667)
- adds doc_content_chars_max argument to WikipediaLoader (#6645)
- chroma nb close img tag (#6669)
- Changed generate_prompt.py (#6644)
- added docstrings where they missed (#6626)
- Wiki loader lint (#6670)
- Just corrected a small inconsistency on a doc page (#6603)
- Fix openapi parameter parsing (#6676)
- Amazon API Gateway hosted LLM (#6673)
- Session deletion method in motorhead memory (#6609)
- Harrison/optional ids opensearch (#6684)
- Add caching to BaseChatModel (issue #1644) (#5089)
- bump to version 213 (#6688)
- Make bs4 a local import in recursive_url_loader.py (#6693)
- bump v214 (#6694)
- split up batch llm calls into separate runs (#5804)
- bump version to 215 (#6719)
- Fix Multi Functions Agent Tracing (#6702)
- fix chroma _similarity_search_with_relevance_scores missing `kwargs` … (#6708)
- Fix Typo in LangChain MyScale Integration Doc (#6705)
- updated sql_database.py for returning sorted table names. (#6692)
- Fix WhatsAppChatLoader : Enable parsing additional formats (#6663)
- feat: added tqdm progress bar to UnstructuredURLLoader (#6600)
- feat: Add `UnstructuredRSTLoader` (#6594)
- beautifulsoup get_text kwargs in WebBaseLoader (#6591)
- Added a MHTML document loader (#6311)
- correct docs site links
- zep api_key auth
<!-- Thank you for contributing to LangChain!
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| 1,773,540,838 |
I_kwDOIPDwls5pthXm
| 6,724 |
DuckDuckGoSearchAPIWrapper Consumes results w/o returning them
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[] | null | 2 | 2023-06-25T22:54:36 | 2023-06-26T13:58:17 | 2023-06-26T13:58:17 |
CONTRIBUTOR
| null |
### System Info
langchain: 0.0.215
platform: ubuntu 22.04 LTS
python: 3.10
### Who can help?
@eyurtsev :)
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://colab.research.google.com/drive/1vraycYUuF-BZ0UA8EoUT0hPt15HoeGPV?usp=sharing
### Expected behavior
The first result of DuckDuckGoSearch to be returned in the `get_snippets` and `results` methods of the DuckDuckGoSearchAPIWrapper.
|
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| 1,773,490,075 |
I_kwDOIPDwls5ptU-b
| 6,723 |
https://python.langchain.com/docs/use_cases/question_answering/document-context-aware-QA code is not working,
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[] | null | 8 | 2023-06-25T21:07:51 | 2023-10-12T16:08:27 | 2023-10-12T16:08:27 |
NONE
| null |
### System Info
colab
### Who can help?
@hwchase17 hi i am trying to use Context aware text splitting and QA / Chat
the code doesnt work starting the vectore indes
# Build vectorstore and keep the metadata
from langchain.vectorstores import Chroma
vectorstore = Chroma.from_documents(texts=all_splits,metadatas=all_metadatas,embedding=OpenAIEmbeddings())
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
i am only following the notebood
### Expected behavior
it should work
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| 1,773,482,364 |
I_kwDOIPDwls5ptTF8
| 6,722 |
Handling larger responses in the API chain
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[] | null | 3 | 2023-06-25T20:44:41 | 2023-12-27T16:07:08 | 2023-12-27T16:07:07 |
NONE
| null |
### Feature request
Currently, the API chain takes the result from the API and gives it directly to a LLM chain. If the result is larger then the context length of the LLM, the chain will give an error. To address this issue I propose that the chain should split the API result using a text splitter, then give the result to combine documents chain that answers the question.
### Motivation
I have found that certain question given to the API chain gives results from the API that exede the context length of the standard openai model. For example:
```python
from langchain.chains import APIChain
from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAI
llm = OpenAI(temperature=0)
from langchain.chains.api import open_meteo_docs
chain_new = APIChain.from_llm_and_api_docs(llm, open_meteo_docs.OPEN_METEO_DOCS, verbose=True)
chain_new.run('What is the weather of [latitude:52.52, longitude:13.419998]?')
```
The LLM creates the this URL: https://api.open-meteo.com/v1/forecast?latitude=52.52&longitude=13.419998&hourly=temperature_2m,weathercode,snow_depth,freezinglevel_height,visibility. The content from this URL is kind of large. This causes the prompt to have 6382 tokens and be lager then the max context length. I think the chain should be able to handle larger responses.
### Your contribution
I can try to submit a PR to implement this solution.
|
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| 1,773,371,161 |
I_kwDOIPDwls5ps38Z
| 6,721 |
ChromaDB Chroma.from_documents error: AttributeError: 'Collection' object has no attribute 'upsert'
|
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[] | null | 2 | 2023-06-25T16:40:21 | 2023-06-25T17:48:54 | 2023-06-25T17:48:54 |
NONE
| null |
### System Info
windows 11, python 3.9.16 , langchain-0.0.215
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The loaded documents succesfully get indexd in FAISS, Deeplake and Pinecone, so I guess the issue is not there. When using Chroma.from_documents the error is thrown:
```
File ~\...langchain\vectorstores\chroma.py:149 in add_texts
self._collection.upsert(
AttributeError: 'Collection' object has no attribute 'upsert'
```
Code:
```python
with open('docs.pkl', 'rb') as file:
documents= pickle.load(file)
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=200)
docs = text_splitter.split_documents(documents)
chroma_dir = base_dir + "ChromaDB" + "_" + str(chunk_size) + "_" + str(overlap)
db2 = Chroma.from_documents(docs, embeddings, persist_directory=chroma_dir)
```
Uploaded the pickle zipped as .pkl is not supported for upload.
[docs.zip](https://github.com/hwchase17/langchain/files/11860249/docs.zip)
### Expected behavior
Index the docs
|
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| 1,773,353,594 |
I_kwDOIPDwls5pszp6
| 6,720 |
search specific vector by tag or key
|
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[] | null | 5 | 2023-06-25T15:52:23 | 2023-10-24T21:10:03 | 2023-10-22T16:07:21 |
NONE
| null |
Hi teams,
Can I make the embedding with tag or any key, I want to make user with different auth. to query specific vector of document.
I was trying to use Redis as vectorstore.Would it recommend to be my vectorstore if I want to implement something authentication.
I'll embed my document into vectorstore and each embedding has its permission. And then I'll have users with different permission. The user who be granted the permission of document can query that vector of document.
For example, Embed three docuemnts into vectorstore and there are two user with different permission.
| Docs | key |
| :----- | :--: |
| Docs_A | A |
| Docs_B | B |
| Docs_C | C |
| User| permission |
| :----- | :--: |
| User_A | A, C |
| User_B | B |
At this use case, User_A can query the vector of Docs_A and Docs_C, and User_B just Docs_B
|
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| 1,773,352,220 |
PR_kwDOIPDwls5T2Pou
| 6,719 |
bump version to 215
|
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[] | null | 1 | 2023-06-25T15:48:57 | 2023-06-25T15:52:52 | 2023-06-25T15:52:52 |
COLLABORATOR
| null | null |
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| 1,773,336,111 |
I_kwDOIPDwls5psvYv
| 6,718 |
DOC: some upgrades to https://langchainers.hashnode.dev/getting-started-with-langchainjs
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[] | null | 1 | 2023-06-25T15:05:51 | 2023-06-25T15:19:55 | 2023-06-25T15:19:54 |
NONE
| null |
### Issue with current documentation:
i've made some comments on https://langchainers.hashnode.dev/getting-started-with-langchainjs
and https://langchainers.hashnode.dev/learn-how-to-integrate-language-models-llms-with-sql-databases-using-langchainjs
some things to update to make it working with the last langchain JS/TS packages
### Idea or request for content:
_No response_
|
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| 1,773,318,895 |
I_kwDOIPDwls5psrLv
| 6,716 |
Pinecone hybrid search is incomplete, missing add_documents method
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[] | null | 2 | 2023-06-25T14:29:47 | 2023-10-01T16:04:53 | 2023-10-01T16:04:52 |
CONTRIBUTOR
| null |
### System Info
All regular retrievers have an add_document method that handles adding a list of documents, and this one retriever only handles add_text, a list of strings and not langchain documents.
For comparison, weaviate hybrid search is able to handle document lists:
` def add_documents(self, docs: List[Document], **kwargs: Any) -> List[str]:
"""Upload documents to Weaviate."""
from weaviate.util import get_valid_uuid
with self._client.batch as batch:
ids = []
for i, doc in enumerate(docs):
metadata = doc.metadata or {}
data_properties = {self._text_key: doc.page_content, **metadata}
# If the UUID of one of the objects already exists
# then the existing objectwill be replaced by the new object.
if "uuids" in kwargs:
_id = kwargs["uuids"][i]
else:
_id = get_valid_uuid(uuid4())
batch.add_data_object(data_properties, self._index_name, _id)
ids.append(_id)
return ids`
### Who can help?
@hw
### 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
Import PineconeHybridSearch
### Expected behavior
retriever.add_documents(DocumentList)
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| 1,773,252,216 |
I_kwDOIPDwls5psa54
| 6,715 |
cannot import name 'get_callback_manager' from 'langchain.callbacks'
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[] | null | 2 | 2023-06-25T12:28:06 | 2023-10-01T16:04:58 | 2023-10-01T16:04:57 |
NONE
| null |
### System Info
Langchain 0.0.125, Python 3.9 within Databricks
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain import PromptTemplate, HuggingFaceHub, LLMChain
### Expected behavior
Installation without issues
|
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| 1,773,197,314 |
I_kwDOIPDwls5psNgC
| 6,714 |
Update the token_max value in mp_reduce.ValueError:"A single document was so long it could not be combined "、"A single document was longer than the context length,"
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[] | null | 3 | 2023-06-25T10:19:42 | 2023-07-05T08:13:50 | 2023-07-05T08:13:50 |
NONE
| null |
### Feature request
The value of token_max in mp_reduce should vary according to the model, not the fixed 3000.
### Motivation
When I use the gpt-3.5-turbo-16k model, I often encounter this ValueError 'A single document was so long it could not be combined' due to the small token_max.I always need to modify a larger value for token_max to resolve the issue.
### Your contribution
I hope to enhance the functionality by dynamically modifying the value of token_max through obtaining the model used by the current chain.
|
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| 1,773,116,562 |
I_kwDOIPDwls5pr5yS
| 6,712 |
A swift langchain copy, for ios or mac apps.
|
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[] | null | 1 | 2023-06-25T07:29:55 | 2023-10-01T16:05:03 | 2023-10-01T16:05:02 |
NONE
| null |
Hi there, awesome project!
https://github.com/buhe/langchain-swift is a swift langchain copy, for ios or mac apps.
Chatbots , QA bot and Agent is done.
Current step:
- [ ] LLMs
- [x] OpenAI
- [ ] Vectorstore
- [x] Supabase
- [ ] Embedding
- [x] OpenAI
- [ ] Chain
- [x] Base
- [x] LLM
- [ ] Tools
- [x] Dummy
- [x] InvalidTool
- [ ] Serper
- [ ] Zapier
- [ ] Agent
- [x] ZeroShotAgent
- [ ] Memory
- [x] BaseMemory
- [x] BaseChatMemory
- [x] ConversationBufferWindowMemory
- [ ] Text Splitter
- [x] CharacterTextSplitter
- [ ] Document Loader
- [x] TextLoader
### Suggestion:
_No response_
|
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I_kwDOIPDwls5pr5d_
| 6,711 |
How can i get execution order inside agent?
|
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[] | null | 2 | 2023-06-25T07:26:30 | 2023-10-01T16:05:08 | 2023-10-01T16:05:08 |
NONE
| null |
I defined some agents and some tools.
How can i get execution order about llm, tool inside agent? Use Callback or other?
If i use callback print, there results is in order?
```
agent_orange = ChatAgent(llm_chain=orange_chain, allowed_tools=tool_names, output_parser=MyChatOutputParser())
agent_chatgpt = ChatAgent(llm_chain=chatgpt_chain, allowed_tools=tool_names,
output_parser=MyChatOutputParser())
agent_executor = MyAgentExecutor.from_agent_and_tools(agent=[agent_orange, agent_chatgpt],
tools=tools,
verbose=True
)
result = agent_executor.run("my query", callbacks=[my_handle])
```
|
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PR_kwDOIPDwls5T1buL
| 6,710 |
Improve performance when retrieving Notion DB pages
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CONTRIBUTOR
| null |
Update to the existing `NotionDBLoader` to reduce duplicate calls to retrieve the metadata information.
When calling out to retrieve the database objects, the property metadata is already returned as part of the request. This change removes the secondary call out, speeding up the process of loading documents from Notion.
@rlancemartin @eyurtsev
|
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I_kwDOIPDwls5pr0yP
| 6,709 |
Error while running on Apple M1 Pro
|
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[] | null | 0 | 2023-06-25T06:37:18 | 2023-06-25T08:28:05 | 2023-06-25T08:28:05 |
NONE
| null |
### System Info
Apple M1
### Who can help?
I get this error : (mach-o file, but is an incompatible architecture (have 'arm64', need 'x86_64'))
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just run the model
### Expected behavior
must run on apple m1
|
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PR_kwDOIPDwls5T1XmU
| 6,708 |
fix chroma _similarity_search_with_relevance_scores missing `kwargs` …
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[] | null | 1 | 2023-06-25T06:18:08 | 2023-06-25T18:53:43 | 2023-06-25T18:53:42 |
CONTRIBUTOR
| null |
Issue: https://github.com/hwchase17/langchain/issues/6707
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I_kwDOIPDwls5prwQj
| 6,707 |
chroma func _similarity_search_with_relevance_scores missing "kwargs" parameter
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[] | null | 2 | 2023-06-25T05:57:30 | 2023-10-01T16:05:14 | 2023-10-01T16:05:13 |
CONTRIBUTOR
| null |
### System Info
LangChain: v0.0.214
### 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
https://github.com/hwchase17/langchain/blob/v0.0.214/langchain/vectorstores/base.py#L410
https://github.com/hwchase17/langchain/blob/v0.0.214/langchain/vectorstores/chroma.py#L225
https://github.com/hwchase17/langchain/blob/v0.0.214/langchain/vectorstores/chroma.py#L198
The `filter` option does not work when search_type is similarity_score_threshold
### Expected behavior
work:
```python
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
return self.similarity_search_with_score(query, k, **kwargs)
```
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| 1,773,070,930 |
I_kwDOIPDwls5prupS
| 6,706 |
ChatOpenAI.get_num_tokens_from_messages raises TypeError with function call response
|
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[] | null | 2 | 2023-06-25T05:36:02 | 2023-10-01T16:05:18 | 2023-10-01T16:05:17 |
NONE
| null |
### System Info
- LangChain version: 0.0.214
- tiktoken version: 0.4.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Executing following code raises `TypeError: expected string or buffer`.
```python
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
llm = ChatOpenAI(model_name="gpt-3.5-turbo-0613", temperature=0)
functions = [
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["location"]
}
}
]
response = llm([HumanMessage(content="What is the weather like in Boston?")], functions=functions)
llm.get_num_tokens_from_messages([response])
```
`get_num_tokens_from_messages` internally converts messages to dict with `_convert_message_to_dict` and then interates all key-value pairs to count the number of tokens.
The code expects value to be a string, but when a function call is included, an exception seems to be raised because value contains a dictionary.
### Expected behavior
As far as I know, there is no officially documented way to calculate the exact token count consumption when using function call.
Someone on the OpenAI forum has [posted](https://community.openai.com/t/how-to-calculate-the-tokens-when-using-function-call/266573/10) a method for calculating the tokens, so perhaps that method could be adopted.
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PR_kwDOIPDwls5T1TTD
| 6,705 |
Fix Typo in LangChain MyScale Integration Doc
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[] | null | 1 | 2023-06-25T05:16:12 | 2023-06-25T18:54:01 | 2023-06-25T18:54:01 |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
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- Models / Prompts: @hwchase17, @dev2049
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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- Description: Fix Typo in LangChain MyScale Integration Doc
@hwchase17
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I_kwDOIPDwls5prscg
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NotImplementedError: WebBaseLoader does not implement lazy_load()
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[] | null | 3 | 2023-06-25T05:06:40 | 2023-10-05T16:08:30 | 2023-10-05T16:08:29 |
NONE
| null |
### Issue you'd like to raise.
when i use RecusiveUrlLoader ,
NotImplementedError: WebBaseLoader does not implement lazy_load()
### Suggestion:
_No response_
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PR_kwDOIPDwls5T1Oz9
| 6,703 |
Add a Pairwise Comparison Chain
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[] | null | 1 | 2023-06-25T04:00:05 | 2023-06-27T03:47:43 | 2023-06-27T03:47:42 |
CONTRIBUTOR
| null |
Notebook shows preference scoring between two chains and reports wilson score interval + p value
I think I'll add the option to insert ground truth labels but doesn't have to be in this PR
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PR_kwDOIPDwls5T1Oq0
| 6,702 |
Fix Multi Functions Agent Tracing
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[] | null | 1 | 2023-06-25T03:58:58 | 2023-06-25T17:39:06 | 2023-06-25T17:39:05 |
CONTRIBUTOR
| null |
Confirmed it works now: https://dev.langchain.plus/public/0dc32ce0-55af-432e-b09e-5a1a220842f5/r
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PR_kwDOIPDwls5T1LcT
| 6,701 |
fix hard code table to self.table_name
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[] | null | 3 | 2023-06-25T03:14:53 | 2023-07-12T23:06:58 | 2023-07-12T23:06:58 |
NONE
| null |
- Description:fix CassandraChatMessageHistory add_message hard code table to self.table_name
Maintainer responsibilities:
- Memory: @hwchase17
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PR_kwDOIPDwls5T1LTB
| 6,700 |
Update arize_callback.py
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[] | null | 1 | 2023-06-25T03:11:41 | 2023-06-25T03:14:49 | 2023-06-25T03:14:45 |
CONTRIBUTOR
| null |
Changed callbackhandler to async
<!-- Thank you for contributing to LangChain!
Replace this comment with:
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If you're adding a new integration, please include:
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2. an example notebook showing its use.
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I_kwDOIPDwls5prjnG
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Memory seems not to be supported in create_pandas_dataframe_agent
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[] | null | 7 | 2023-06-25T03:05:36 | 2023-12-13T16:08:48 | 2023-12-13T16:08:47 |
NONE
| null |
### Feature request
I am trying to add memory to create_pandas_dataframe_agent to perform post processing on a model that I trained using Langchain. I am using the following code at the moment.
```
from langchain.llms import OpenAI
import pandas as pd
df = pd.read_csv('titanic.csv')
agent = create_pandas_dataframe_agent(OpenAI(temperature=0), [df], verbose=True)
```
I tried adding memory = ConversationBufferMemory(memory_key="chat_history") but that didnt help. Tried many other methods but seems like the memory for create_pandas_dataframe_agent is not implemented
### Motivation
There is a major need in pandas processing to save models as pickle files along with adding new features to the studied dataset which alters the original dataset for the next step. It seems like langchain currently doesnt support that.
### Your contribution
I can help with the implementation if necessary.
|
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PR_kwDOIPDwls5T1Hp4
| 6,698 |
Improve vector store onboarding exp
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[] | null | 2 | 2023-06-25T02:08:14 | 2023-07-18T20:48:42 | 2023-07-18T20:48:42 |
CONTRIBUTOR
| null |
This PR
- fixes the `similarity_search_by_vector` example, makes the code run and adds the example to mirror `similarity_search`
- reverts back to chroma from faiss to remove sharp edges / create a happy path for new developers. (1) real metadata filtering, (2) expected functionality like `update`, `delete`, etc to serve beyond the most trivial use cases
@hwchase17
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PR_kwDOIPDwls5T0_XY
| 6,697 |
tiktoken override
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[] | null | 2 | 2023-06-24T23:56:01 | 2023-06-26T07:49:34 | 2023-06-26T07:49:33 |
COLLABORATOR
| null | null |
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I_kwDOIPDwls5prUMM
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Chroma.from_documents(...persist_directory=db_path) has no effect
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[] | null | 3 | 2023-06-24T23:24:49 | 2023-10-29T16:05:41 | 2023-10-29T16:05:41 |
NONE
| null |
### System Info
$ pip freeze | grep langchain
langchain==0.0.197
langchainplus-sdk==0.0.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
For some reason :
db = Chroma.from_documents(texts, self.embeddings, persist_directory=db_path, client_settings=settings)
persist_directory=db_path, has no effect ... upon db.persist() it stores into the default directory 'db', instead of using db_path.
Only if you explicitly set Settings(persist_directory=db_path, ... ) it works.
Probable reason is that in langchain chroma.py if you pass client_settings and 'persist_directory' is not part of the settings, it will not add 'persist_directory' as it is done in the ELSE case, but ...:
(line 77 ++)
```
if client is not None:
self._client = client
else:
if client_settings:
self._client_settings = client_settings <<< .... here ..........
else:
self._client_settings = chromadb.config.Settings()
if persist_directory is not None:
self._client_settings = chromadb.config.Settings(
chroma_db_impl="duckdb+parquet",
persist_directory=persist_directory,
)
self._client = chromadb.Client(self._client_settings)
self._embedding_function = embedding_function
self._persist_directory = persist_directory
```
but ... chromadb.__init__.py expects 'persist_directory' in settings i.e. (line 44), otherwise it will use the default :
```
elif setting == "duckdb+parquet":
require("persist_directory")
logger.warning(
f"Using embedded DuckDB with persistence: data will be stored in: {settings.persist_directory}"
)
```
### Expected behavior
db = Chroma.from_documents(texts, self.embeddings, persist_directory=db_path, client_settings=settings)
should use db_path instead of 'db'
|
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I_kwDOIPDwls5prTCL
| 6,695 |
Issue: create_csv_agent() error when loading a list of csv files
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[] | null | 7 | 2023-06-24T23:06:32 | 2023-07-08T15:24:50 | 2023-07-08T15:24:49 |
NONE
| null |
### System Info
Langchain 0.0.214
Python 3.10
Ubuntu 22.04
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
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- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
agent = create_csv_agent(
ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613"),
["titanic.csv", "titanic_age_fillna.csv"],
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
)
agent.run("how many rows in the age column are different between the two dfs?")
Got error: ValueError: Invalid file path or buffer object type:
### Expected behavior
According to Langchain documentation https://python.langchain.com/docs/modules/agents/toolkits/csv.html, the CSV agent "can interact with multiple csv files passed in as a list", and it should not generate an error.
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| 6,694 |
bump v214
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[] | null | 1 | 2023-06-24T21:09:28 | 2023-06-24T21:23:12 | 2023-06-24T21:23:11 |
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PR_kwDOIPDwls5T0zop
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Make bs4 a local import in recursive_url_loader.py
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[] | null | 1 | 2023-06-24T20:49:05 | 2023-06-24T20:54:11 | 2023-06-24T20:54:10 |
COLLABORATOR
| null |
Resolve https://github.com/hwchase17/langchain/issues/6679
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PR_kwDOIPDwls5T0vr1
| 6,692 |
updated sql_database.py for returning sorted table names.
|
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[] | null | 1 | 2023-06-24T20:03:22 | 2023-06-25T19:04:25 | 2023-06-25T19:04:24 |
CONTRIBUTOR
| null |
Added code to get the tables info in sorted order in methods get_usable_table_names and get_table_info.
Linked to Issue: #6640
|
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| 1,772,877,397 |
I_kwDOIPDwls5pq_ZV
| 6,691 |
sitemap loader throws error TypeError: _request() got an unexpected keyword argument 'verify', many docs refer to wrong links for sitemap as well.
|
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[] | null | 8 | 2023-06-24T19:11:29 | 2023-10-24T16:07:28 | 2023-10-24T16:07:27 |
NONE
| null |
### System Info
windows 11 python 3.9.16 langchain 0.0.212
### Who can help?
Code from https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/sitemap
```python
from langchain.document_loaders.sitemap import SitemapLoader
sitemap_loader = SitemapLoader(web_path="https://langchain.readthedocs.io/sitemap.xml")
docs = sitemap_loader.load()
```
throws:
```python
self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, **kwargs)
TypeError: _request() got an unexpected keyword argument 'verify'
```python
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders.sitemap import SitemapLoader
sitemap_loader = SitemapLoader(web_path="https://langchain.readthedocs.io/sitemap.xml")
docs = sitemap_loader.load()
### Expected behavior
to work or get a doc update
|
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I_kwDOIPDwls5pq-b9
| 6,690 |
Agent knows how to correctly proceed, but uses tool incorrectly anyways
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[] | null | 2 | 2023-06-24T18:58:15 | 2023-10-06T16:07:04 | 2023-10-06T16:07:03 |
NONE
| null |
### System Info
platform: macOS-13.2.1-arm64-arm-64bit
Python 3.11.3
langchain 0.0.212
langchainplus-sdk 0.0.17
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm trying to get my Agent to correctly use my tools - from it's internal dialogue I can see it knows it's about to use a tool incorrectly but then it just goes ahead and does it resulting in an exception from my arg schema.
Here's the input and output with my agent:
---
💬: add a todo to by tortillas to my grocery shopping todo
> Entering new chain...
Action:
```
{
"action": "save_sub_todo",
"action_input": {
"name": "Buy tortillas",
"tags": ["grocery shopping"],
"parent_id": "ID of the grocery shopping todo"
}
}
```
Replace "ID of the grocery shopping todo" with the actual ID of the todo for grocery shopping. You can use `get_all_todos()` to find the ID if you don't know it.
```
Traceback (most recent call last):
File "/Users/jacobbrooks/PythonProjects/jbbrsh/term.py", line 32, in <module>
Fire(run)
File "/Users/jacobbrooks/PythonProjects/jbbrsh/.venv/lib/python3.11/site-packages/fire/core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/jacobbrooks/PythonProjects/jbbrsh/.venv/lib/python3.11/site-packages/fire/core.py", line 475, in _Fire
component, remaining_args = _CallAndUpdateTrace(
^^^^^^^^^^^^^^^^^^^^
File "/Users/jacobbrooks/PythonProjects/jbbrsh/.venv/lib/python3.11/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/jacobbrooks/PythonProjects/jbbrsh/term.py", line 24, in run
response = agent.run(user_input)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/jacobbrooks/PythonProjects/jbbrsh/.venv/lib/python3.11/site-packages/langchain/chains/base.py", line 290, in run
return self(args[0], callbacks=callbacks, tags=tags)[_output_key]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
```
Here are the tools I'm passing to my agent:
---
```python
class TodoInput(BaseModel):
name: str = Field(description="Todo name")
tags: List[str] = Field(description="Tags to categorize todos")
@tool(return_direct=False, args_schema=TodoInput)
def save_todo(name: str, tags: List[str]) -> str:
"""
Saves a todo for the user
The string returned contians the todos name and its ID. The ID can be used to add child todos.
"""
notion_todo = NotionTodo.new(
notion_client=_notion,
database_id=DATABASE_ID,
properties={
"Tags": tags,
"Name": name
}
)
_notion.todos.append(notion_todo)
return f"todo saved: {notion_todo}"
class SubTodoInput(TodoInput):
parent_id: str = Field(
description="ID for parent todo, only needed for sub-todos",
)
@root_validator
def validate_query(cls, values: Dict[str, Any]) -> Dict:
parent_id = values["parent_id"]
if re.match(r"[\d\w]{8}-[\d\w]{4}-[\d\w]{4}-[\d\w]{4}-[\d\w]{12}", parent_id) is None:
raise ValueError(f'Invalid parent ID "{values["parent_id"]}"')
return values
@tool(return_direct=False, args_schema=SubTodoInput)
def save_sub_todo(name: str, tags: List[str], parent_id:str) -> str:
"""
Saves a child todo with a parent todo for the user
The string returned contians the todos name and its ID, The ID is formatted like so "f1ab8b74-6b67-46b1-81ec-519805c7a1cb"
Do not make up IDs! use get_all_todos to find the best ID if a real one is unavailable.
"""
notion_todo = NotionTodo.new(
notion_client=_notion,
database_id=DATABASE_ID,
properties={
"Tags": tags,
"Name": name,
"Parent todo": parent_id
}
)
_notion.todos.append(notion_todo)
return f"todo saved: {notion_todo}"
@tool
def get_all_todos():
"""
Returns a list of all existing todos.
Useful for finding an ID for an existing todo when you have to adda child todo.
"""
return '\n'.join([
f"'{t.name}' {'Complete' if t.complete else 'Incomplete'} id={t.id} parent_id={t.parent_todo}"
for t in _notion.todos
])
```
Here's how I'm creating my agent
---
```python
_system_message = """
Your purpose is to store and track todos for your user
When using Tools with ID arguments don't make up IDs! Find the best ID from looking through all todos.
"""
llm = ChatOpenAI(temperature=0)
memory_key = "chat_history"
chat_history = MessagesPlaceholder(variable_name=memory_key)
memory = ConversationBufferMemory(memory_key=memory_key, return_messages=True)
agent = initialize_agent(
llm=llm,
tools = [HumanInputRun(), *tools],
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
memory = memory,
agent_kwargs={
"system_message": _system_message,
"memory_prompts": [chat_history],
"input_variables": [
"input", "agent_scratchpad", memory_key
]
},
**kwargs
)
return agent
```
### Expected behavior
The bot init's internal dialogue _knows_ that it's about to use an invalid ID, and it knows how to go about getting the real ID
> Replace "ID of the grocery shopping todo" with the actual ID of the todo for grocery shopping. You can use `get_all_todos()` to find the ID if you don't know it.
But it just goes ahead and uses the tool resulting in an exception.
The Agent should acknowledge this insight, use the tool it knows it should use to get the proper ID, and then reformat the initial tool attempt with the legitimate ID.
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| 1,772,873,282 |
PR_kwDOIPDwls5T0rOV
| 6,689 |
document loader as a retriever
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[] | null | 4 | 2023-06-24T18:57:39 | 2023-07-19T23:15:04 | 2023-07-19T23:14:38 |
COLLABORATOR
| null |
This unbraking change allows using every Document loader as a Retriever without any additional changes.
This change effectively increased number of the available retrievers from ~20 to 80+
`docs/extras/modules/data_connection/retrievers/how_to/document_loaders_as_retrievers.ipynb` provides explanations and a usage example.
Maintainer responsibilities:
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
It can be interesting for @hwchase17 and @dev2049
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| 1,772,869,683 |
PR_kwDOIPDwls5T0qhs
| 6,688 |
bump to version 213
|
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I_kwDOIPDwls5pq9I-
| 6,687 |
Issue: Can an LLM be used as a tool?
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[] | null | 4 | 2023-06-24T18:41:11 | 2023-11-07T03:29:34 | 2023-10-02T16:06:24 |
NONE
| null |
### Issue you'd like to raise.
I have a use case where the agent is supposed to perform certain activities (going over the metadata and telling if the currently selected column is fit for querying). This would need a `zero-shot-react-agent` to use several LLMs as tools instead of the present ones (like search being shown everywhere). The [documentation](https://python.langchain.com/docs/modules/agents/how_to/custom_mrkl_agent#multiple-inputs) shows that this is possible but is in itself quite ambiguous.
How do I create an LLMChain as a tool if it always needs a prompt while initialisation? And the prompt can be created only after mentioning this LLMChain as a tool in the `create_prompt` function?
### Suggestion:
_No response_
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| 1,772,858,939 |
PR_kwDOIPDwls5T0obH
| 6,686 |
align chroma vectorstore get with chromadb to enable where filtering
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CONTRIBUTOR
| null |
allows for where filtering on collection via get
- Description: aligns langchain chroma vectorstore get with underlying [chromadb collection get](https://github.com/chroma-core/chroma/blob/main/chromadb/api/models/Collection.py#L103) allowing for where filtering, etc.
- Issue: NA
- Dependencies: none
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @pappanaka
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PR_kwDOIPDwls5T0ZwP
| 6,684 |
Harrison/optional ids opensearch
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[] | null | 1 | 2023-06-24T15:34:03 | 2023-06-24T16:19:58 | 2023-06-24T16:19:57 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5T0IhM
| 6,683 |
Fix conversational_retrieval combining with chat history affects the question and answer experience
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[] | null | 2 | 2023-06-24T13:37:48 | 2023-07-05T07:48:20 | 2023-07-05T07:48:20 |
NONE
| null |
Fixes # (issue)
While using the ConversationalRetrievalChain, and specifically applying both condense_question_prompt and combine_docs_chain_kwargs parameters, I followed the official documentation to pass chat_history when invoking this chain. The program runs as expected, but the Q&A experience is less than optimal. I've identified two main issues:
When summarizing the question using condense_question, the response prompt generated by load_qa_chain is treated as 'Human' rather than 'AI'. Currently, I am remedying this by determining whether to pass the question to human_message or ai_message, based on whether chat_history is provided through an external interface.
Upon reviewing the code, I noticed that the answer is first generated by self.question_generator, but in reality, the desired answer initially comes from the prompt in combine_docs_chain_kwargs. However, this response is then re-fed into self.combine_docs_chain, leading to a situation of self-posed questions and self-given answers, which isn't ideal. The final answer from combine_docs_chain isn't what I'm looking for, and I see no need to go through another loop only to end up with an unsatisfactory answer. Thus, I wish to adjust the logic for obtaining the answer in the call and acall methods.
While my implementation might not be the most ideal, it considerably enhances the Q&A experience based on chat history in my project. I hope for official support on this issue and appreciate your help. Thank you!
Before submitting
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I_kwDOIPDwls5ppyZU
| 6,681 |
Issue: What software was used to create the flowcharts in the document?
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[] | null | 1 | 2023-06-24T09:21:32 | 2023-09-30T16:05:07 | 2023-09-30T16:05:07 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I would like to know what software was used to create the flowcharts in the document. They look very beautiful.
### Suggestion:
_No response_
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I_kwDOIPDwls5ppqzK
| 6,680 |
Help for an error that appears in the CONVERSATIONAL Agent
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NONE
| null |
### Issue you'd like to raise.
This problem occurred when I tried to use the tool support for a conversation-type agent using an agent of type SRUCTURED_CHAT_SHOT_REACT_DESCRIPTION. Here is some of the source code and errors
env:
Python 3.10
Langchain Lastest
WIN 10 Profession Lastest
### Suggestion:
_No response_
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crash because of missing bs4 dependency in version 2.12
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[] | null | 3 | 2023-06-24T06:31:34 | 2023-06-24T20:54:12 | 2023-06-24T20:54:12 |
CONTRIBUTOR
| null |
### System Info
Langchain: 2.12
commit: #6455
version 2.11 does niet have this
### Who can help?
@rlancemartin, @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
workaround: install bs4 manually (pip install bs4)
`from langchain.agents import initialize_agent, AgentType`
leads to:
```
File "//main.py", line 17, in <module>
from langchain.agents import initialize_agent, AgentType
File "/usr/local/lib/python3.11/site-packages/langchain/__init__.py", line 6, in <module>
from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
File "/usr/local/lib/python3.11/site-packages/langchain/agents/__init__.py", line 2, in <module>
from langchain.agents.agent import (
File "/usr/local/lib/python3.11/site-packages/langchain/agents/agent.py", line 16, in <module>
from langchain.agents.tools import InvalidTool
File "/usr/local/lib/python3.11/site-packages/langchain/agents/tools.py", line 8, in <module>
from langchain.tools.base import BaseTool, Tool, tool
File "/usr/local/lib/python3.11/site-packages/langchain/tools/__init__.py", line 3, in <module>
from langchain.tools.arxiv.tool import ArxivQueryRun
File "/usr/local/lib/python3.11/site-packages/langchain/tools/arxiv/tool.py", line 12, in <module>
from langchain.utilities.arxiv import ArxivAPIWrapper
File "/usr/local/lib/python3.11/site-packages/langchain/utilities/__init__.py", line 3, in <module>
from langchain.utilities.apify import ApifyWrapper
File "/usr/local/lib/python3.11/site-packages/langchain/utilities/apify.py", line 5, in <module>
from langchain.document_loaders import ApifyDatasetLoader
File "/usr/local/lib/python3.11/site-packages/langchain/document_loaders/__init__.py", line 97, in <module>
from langchain.document_loaders.recursive_url_loader import RecusiveUrlLoader
File "/usr/local/lib/python3.11/site-packages/langchain/document_loaders/recursive_url_loader.py", line 5, in <module>
from bs4 import BeautifulSoup
ModuleNotFoundError: No module named 'bs4'
```
requirements.txt:
```
openai==0.27.8
fastapi==0.97.0
websockets==11.0.3
pydantic==1.10.9
langchain==0.0.212
uvicorn[standard]
jinja2
lancedb==0.1.8
itsdangerous
tiktoken==0.4.0
```
### Expected behavior
I think
from bs4 import BeautifulSoup
in recursive_url_loader.py
should have been a local import
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I_kwDOIPDwls5ppfW1
| 6,678 |
Problem to run on linux but not on windows
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[] | null | 2 | 2023-06-24T06:10:07 | 2023-09-30T16:05:18 | 2023-09-30T16:05:17 |
NONE
| null |
### System Info
see: https://github.com/hwchase17/langchain/discussions/1533
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
use FAISS on windows and next try to reuse those embeddings on lunix
### Expected behavior
```
I have a problem, when I want to run a project with langchain on windows, everything works perfectly, and with the same conditions on linux (libraries, python version, etc.) it doesn't work and throws this error, does anyone know what it could be?
2023-03-08 15:13:24 Failed to run listener function (error: search() missing 3 required positional arguments: 'k', 'distances', and 'labels')
2023-03-08 15:13:24 Traceback (most recent call last):
2023-03-08 15:13:24 File "/usr/local/lib/python3.9/site-packages/slack_bolt/listener/thread_runner.py", line 120, in run_ack_function_asynchronously
2023-03-08 15:13:24 listener.run_ack_function(request=request, response=response)
2023-03-08 15:13:24 File "/usr/local/lib/python3.9/site-packages/slack_bolt/listener/custom_listener.py", line 50, in run_ack_function
2023-03-08 15:13:24 return self.ack_function(
2023-03-08 15:13:24 File "//./main.py", line 28, in question
2023-03-08 15:13:24 response = processQuestion(query)
2023-03-08 15:13:24 File "/api.py", line 42, in processQuestion
2023-03-08 15:13:24 sources = doSimilaritySearch(index, query)
2023-03-08 15:13:24 File "/utils.py", line 87, in doSimilaritySearch
2023-03-08 15:13:24 docs = indexFaiss.similarity_search(query, k=5)
2023-03-08 15:13:24 File "/usr/local/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 166, in similarity_search
2023-03-08 15:13:24 docs_and_scores = self.similarity_search_with_score(query, k)
2023-03-08 15:13:24 File "/usr/local/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 136, in similarity_search_with_score
2023-03-08 15:13:24 docs = self.similarity_search_with_score_by_vector(embedding, k)
2023-03-08 15:13:24 File "/usr/local/lib/python3.9/site-packages/langchain/vectorstores/faiss.py", line 110, in similarity_search_with_score_by_vector
2023-03-08 15:13:24 scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
2023-03-08 15:13:24 TypeError: search() missing 3 required positional arguments: 'k', 'distances', and 'labels'
```
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I_kwDOIPDwls5ppW7A
| 6,677 |
Issue: ConversationalRetrievalChain Fails to Distinguish User's Intention for Chat History Only or Chat History + Vector Store Answer
|
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[] | null | 5 | 2023-06-24T04:38:11 | 2023-10-30T16:06:18 | 2023-10-30T16:06:18 |
NONE
| null |
### Issue you'd like to raise.
In my application, I am using the ConversationalRetrievalChain with "stuff" chain type, FAISS as the vector store and ConversationBufferMemory. I have noticed that when I ask a question related to a previous response (i.e. a request that is only looking for answers from chat history, such as a summarization request), the system still searches for answers from both the vector store and chat history. This often leads to incorrect or irrelevant answers (the summarization will include data from chat history as well as data that is never presented in previous conversation from vector store).
I've tried to address this issue by passing a custom prompt using combine_docs_chain_kwargs to specify whether a response should be generated based on the chat history only, or the chat history and the vector store. However, this approach hasn't been effective.
It seems that the system is currently unable to correctly discern the user's intention to exclusively use the chat history for generating a response. It's crucial that the system can accurately determine this to provide relevant and accurate responses.
### Suggestion:
I propose that the system should be enhanced with a mechanism to first detect the user's intention to either:
Select a response from the chat history only, or
Select a response from the chat history in combination with the vector store.
This could possibly be achieved by conditionally including or excluding certain parts of a prompt, such as {context} (from stuff_prompt.py, this is the default prompt used by stuff chain type), based on user input or intentions. However, this logic would need to be implemented during the data preparation for the template.
I haven't figured out a way to do so. Looking forward to your help!
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PR_kwDOIPDwls5TzNrH
| 6,676 |
Fix openapi parameter parsing
|
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[] | null | 1 | 2023-06-24T04:06:27 | 2023-06-24T04:19:13 | 2023-06-24T04:19:12 |
CONTRIBUTOR
| null |
Ensure parameters are json serializable, related to #6671
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| 1,772,436,936 |
I_kwDOIPDwls5ppT3I
| 6,675 |
[ChatVertexAI] 'ChatSession' object has no attribute '_history'
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[] | null | 4 | 2023-06-24T04:02:32 | 2023-10-02T16:06:29 | 2023-10-02T16:06:29 |
NONE
| null |
### System Info
```python
Linux-6.3.7-1-default-x86_64-with-glibc2.37
Python Version: 3.10.11
Langchain Version: 0.0.21
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Input
```Python
from langchain.chat_models import ChatVertexAI
from langchain.schema import HumanMessage, SystemMessage, AIMessage
chat = ChatVertexAI(temperature=0.7,verbose=True)
chat(
[
SystemMessage(content="Assume you are an expert tour guide.Help the user and assist him in his travel"),
HumanMessage(content="I like lush green valleys with cool weather. Where should I go?"),
AIMessage(content="Switzerland is a nice place to visit"),
HumanMessage(content="Name some of the popular places there to visit")
]
)
```
Response
```python
File [~/gamedisk/PyTorch2.0_env/lib/python3.10/site-packages/langchain/chat_models/vertexai.py:136], in ChatVertexAI._generate(self, messages, stop, run_manager, **kwargs)
134 chat = self.client.start_chat(**params)
135 for pair in history.history:
--> 136 chat._history.append((pair.question.content, pair.answer.content))
137 response = chat.send_message(question.content, **params)
138 text = self._enforce_stop_words(response.text, stop)
AttributeError: 'ChatSession' object has no attribute '_history'
```
### Expected behavior
It is expected to return an object similar to this
```python
AIMessage(content='Lauterbrunnen is a nice place to visit', additional_kwargs={}, example=False)
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I_kwDOIPDwls5ppTv5
| 6,674 |
Issue: using different local models for QA generation
|
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[] | null | 1 | 2023-06-24T04:02:09 | 2023-09-30T16:05:28 | 2023-09-30T16:05:27 |
NONE
| null |
### Issue you'd like to raise.
with the QA generation over a document store is it possible to use hugging face models (local) instead of chat open AI?
### Suggestion:
_No response_
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PR_kwDOIPDwls5TzGXd
| 6,673 |
Amazon API Gateway hosted LLM
|
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[] | null | 2 | 2023-06-24T02:31:25 | 2023-06-24T04:27:26 | 2023-06-24T04:27:25 |
CONTRIBUTOR
| null |
This PR adds a new LLM class for the Amazon API Gateway hosted LLM. The PR also includes example notebooks for using the LLM class in an Agent chain.
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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| 1,772,277,501 |
PR_kwDOIPDwls5TyqHv
| 6,672 |
add optional ids param to opensearch
|
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[] | null | 1 | 2023-06-23T23:10:08 | 2023-06-24T20:43:59 | 2023-06-24T15:32:23 |
CONTRIBUTOR
| null |
- Description: add optional ids to allow updating existing opensearch documents, follows pattern of other vector stores
- Issue: #5952
- Dependencies: None
- Tag maintainer: @rlancemartin, @eyurtsev
|
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| 1,772,260,655 |
I_kwDOIPDwls5poo0v
| 6,671 |
openapi_spec_to_openai_fn generates Date objects which are not JSON serializable
|
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[] | null | 3 | 2023-06-23T22:52:10 | 2023-09-30T16:05:33 | 2023-09-30T16:05:32 |
CONTRIBUTOR
| null |
### System Info
v0.0.211
### Who can help?
@hw
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use an OpenAPI spec which contains a key `format` with a known type like `date`.
```yaml
date_de_naissance_dirigeant_min:
name: date_de_naissance_dirigeant_min
in: query
description: Date de naissance minimale du dirigeant (ou de l'un des dirigeants de l'entreprise pour une recherche d'entreprises), au format JJ-MM-AAAA.
required: false
schema:
type: string
format: date
example: 1970-01-01
```
This gets translated into
```python
'date_de_naissance_dirigeant_min': {
'type': 'string',
'schema_format': 'date',
'description': "Date de naissance minimale du dirigeant (ou de l'un des dirigeants de l'entreprise pour une recherche d'entreprises), au format JJ-MM-AAAA.",
'example': datetime.date(1970, 1, 1),
},
```
### Expected behavior
No objects other that strings and lists should be instanciated by `openapi_spec_to_openai_fn`
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PR_kwDOIPDwls5Tylxe
| 6,670 |
Dev2049/wiki lint
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CONTRIBUTOR
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PR_kwDOIPDwls5Tyges
| 6,669 |
chroma nb close img tag
|
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[] | null | 1 | 2023-06-23T22:19:48 | 2023-06-23T22:41:55 | 2023-06-23T22:41:54 |
CONTRIBUTOR
| null | null |
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| 1,772,215,395 |
PR_kwDOIPDwls5TycRr
| 6,668 |
Add support for tags in chain group context manager
|
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[] | null | 1 | 2023-06-23T22:02:23 | 2023-06-26T17:37:34 | 2023-06-26T17:37:34 |
CONTRIBUTOR
| null |
Lets you specify local and inheritable tags in the group manager.
Also, add more verbose docstrings for our reference docs.
|
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PR_kwDOIPDwls5TyYfu
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openapi -> openai nit
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Dev2049/bump 212
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update chroma notebook
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CONTRIBUTOR
| null |
@rlancemartin I updated the notebook for Chroma to hopefully be a lot easier for users.
Let me know what you think
Image preview:
<img width="666" alt="Screenshot 2023-06-23 at 1 29 56 PM" src="https://github.com/hwchase17/langchain/assets/891664/7f613a84-35f5-4502-982d-85d5f9f3564a">
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PR_kwDOIPDwls5Tx8mQ
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Fix WhatsAppChatLoader : Enable parsing additional formats
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CONTRIBUTOR
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- Description: Updated regex to support a new format that was observed when whatsapp chat was exported.
- Issue: #6654
- Dependencies: No new dependencies
- Tag maintainer: @rlancemartin, @eyurtsev
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PR_kwDOIPDwls5TxeAg
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Arthur callback handler
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CONTRIBUTOR
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- Description: adding a callback handler to log LLM inferences with the Arthur monitoring and observability platform. also adding a notebook demonstrating usage
- Dependencies: the arthurai package is a new dependency, optional for normal langchain users but needed to use the Arthur callback handler
- Twitter handle: @maxcembalest
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openapi_openai docstring
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bump 211
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Create merge loader that combines documents from a set of loaders
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COLLABORATOR
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Simple utility loader that combines documents from a set of specified loaders.
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[] | null | 1 | 2023-06-23T17:48:03 | 2023-06-23T18:00:35 | 2023-06-23T18:00:34 |
CONTRIBUTOR
| null | null |
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| 1,771,741,425 |
I_kwDOIPDwls5pmqDx
| 6,657 |
Chromadb only returns the first document from persistent db
|
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[] | null | 3 | 2023-06-23T16:36:06 | 2023-12-15T12:38:44 | 2023-10-02T16:06:35 |
NONE
| null |
### System Info
|software|Version|
|:---:|:---:|
|python|3.10.11|
|LangChain|0.0.209|
|Chroma|0.3.26|
|Windows|11|
|Ubuntu|22.06|
I have tried on both windows and ubuntu
### Who can help?
@eyurtsev
### 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
i am having trouble adding multiple documents into a vectordb. I am using chromadb here.
The following loads, split and embed 2 text files and store them in a persistant vector database. Then it queries the database.
```python
from langchain.document_loaders import TextLoader
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
import os
from getpass import getpass
OPENAI_API_KEY = getpass()
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
embeddings = OpenAIEmbeddings()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
persist_directory = "db"
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
# -------------------------- Adding spacex_wiki.txt -------------------------- #
loader = TextLoader("this_has_to_work/spacex_wiki.txt", encoding="utf8")
documents = loader.load()
docs = text_splitter.split_documents(documents)
db.add_documents(docs)
# ------------------------- Adding imploson_wiki.txt ------------------------- #
loader = TextLoader("this_has_to_work/implosion_wiki.txt", encoding="utf8")
documents = loader.load()
docs = text_splitter.split_documents(documents)
db.add_documents(docs)
db.persist()
# --------------------------- querying the vectordb -------------------------- #
db = None
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
retriever = db.as_retriever(search_type="mmr")
query = "What is implosion?"
print(query)
print(retriever.get_relevant_documents(query)[0])
print("\n\n")
query = "Who is elon?"
print(query)
print(retriever.get_relevant_documents(query)[0])
print("\n\n")
```
The above code runs without a problem and is able to retreive from both text file. The problem starts with the following code.
The following code only loads the vectordb from the persistant location.
```python
from langchain.document_loaders import TextLoader
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
import os
from getpass import getpass
OPENAI_API_KEY = getpass()
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
embeddings = OpenAIEmbeddings()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
persist_directory = "db"
db = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
# --------------------------- querying the vectordb -------------------------- #
retriever = db.as_retriever(search_type="mmr")
query = "What is implosion?"
print(query)
print(retriever.get_relevant_documents(query)[0])
print("\n\n")
query = "Who is elon?"
print(query)
print(retriever.get_relevant_documents(query)[0])
print("\n\n")
```
The above code will only return from the first document stored in the vectordb (spacex_wiki.txt), no matter what the prompt is.
The following are the text files used.
[implosion_wiki.txt](https://github.com/hwchase17/langchain/files/11850301/implosion_wiki.txt)
[spacex_wiki.txt](https://github.com/hwchase17/langchain/files/11850303/spacex_wiki.txt)
### Expected behavior
It is expected that information from both documents can be retreived when the vectordb is loaded from persistant location.
However, only the first embedded document can be retreived.
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PR_kwDOIPDwls5Twy9n
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bump 210
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CONTRIBUTOR
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| 1,771,671,290 |
I_kwDOIPDwls5pmY76
| 6,654 |
WhatsAppChatLoader doesn't extract messages exported from WhatsApp
|
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[] | null | 0 | 2023-06-23T15:42:11 | 2023-06-26T09:16:16 | 2023-06-26T09:16:16 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.208
Archcraft x86_64
Python 3.11.3
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Vector Stores / Retrievers
- [X] Document Loaders
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Export Chat from Whatsapp
2. The exported chat contains messages that aren't being extracted by the regex. Example :
`7/19/22, 11:26 pm - User: Message`
https://github.com/hwchase17/langchain/blob/980c8651743b653f994ad6b97a27b0fa31ee92b4/langchain/document_loaders/whatsapp_chat.py#L43
There are two issues here:
1. The regex is looking for a space character but in my exported message there was a unicode NNBSP character (U+202F)
2. AM/PM are expected in capital case whereas my export was in small case.
### Expected behavior
Message is parsed successfully.
|
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| 1,771,536,293 |
PR_kwDOIPDwls5TwKJr
| 6,652 |
ChatVertexAI broken - Fix error with sending context in params
|
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[] | null | 5 | 2023-06-23T14:13:43 | 2023-06-24T16:05:31 | 2023-06-23T20:38:21 |
CONTRIBUTOR
| null |
vertex Ai chat is broken right now. That is because context is in params and chat.send_message doesn't accept that as a params.
- Closes issue [ChatVertexAI Error: _ChatSessionBase.send_message() got an unexpected keyword argument 'context' #6610](https://github.com/hwchase17/langchain/issues/6610)
@dev2049
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I_kwDOIPDwls5pl2s-
| 6,651 |
Allow users to purchase tokens for more search results
|
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[] | null | 1 | 2023-06-23T14:10:15 | 2023-09-29T16:05:33 | 2023-09-29T16:05:32 |
CONTRIBUTOR
| null |
### Feature request
Since the doc refactor, users are now limited to four search results per query.
<img width="837" alt="image" src="https://github.com/hwchase17/langchain/assets/1082786/e340d78c-b8bb-4242-93bc-0d96d4514b44">
I think users should be able to purchase tokens if they would like to be able to access more search results, like perhaps 1 token per 10 extra results. This would enable functionality similar to the previous search functions, which would return up to 50 results.
Tokens could also be used for respecting `@media (prefers-color-scheme: dark)` since right now my laptop is not rated high enough for the default brightness and I would not like to blow out my display. Lastly, I would be willing to pay 5 tokens to increase the font weight, since it is unfortunately not very accessible for people with low vision, though that price should probably be determined by what the market will bear.
### Motivation
Motivation: find broad array code definitions and usage examples when trying to integrate a piece of the library into my application.
Related to #6300.
Will allow users to surface relevant information without having to implement custom crawler/indexer for the docs.
### Your contribution
I will happily serve as QA tester to test the amount of search results returned. I don't think my sunglasses offer enough protection to test whether the docs site respects the dark-mode CSS media query.
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I_kwDOIPDwls5pl2iC
| 6,650 |
[AzureChatOpenAI] openai_api_type can't be changed from the default 'azure' value
|
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NONE
| null |
### System Info
Hello,
during the development of an application that needs to authenticate to Azure services and use the wrapper [AzureChatOpenAi](https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/azure_openai.py), we encountered an error due to the fact that the model could not use the 'azure_ad' type.
It seems that this class sets the openai_api_type always to the set default value of 'azure' even if we have an environment variable called 'OPENAI_API_TYPE' specifying 'azure_ad'.
Why is it so?
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
answering_llm=AzureChatOpenAI(
deployment_name=ANSWERING_MODEL_CONFIG.model_name,
model_name=ANSWERING_MODEL_CONFIG.model_type, #"gpt-3.5-turbo"
openai_api_type="azure_ad", # IF THIS IS NOT EXPLICITLY PASSED IT FAILS
openai_api_key=auth_token,
temperature=ANSWERING_MODEL_CONFIG.temperature,
max_tokens=ANSWERING_MODEL_CONFIG.max_tokens
)
### Expected behavior
We expect the wrapper to take the value of the environmental variable correctly.
|
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https://github.com/langchain-ai/langchain/pull/6649
| 1,771,492,635 |
PR_kwDOIPDwls5TwAr5
| 6,649 |
Openning up documentation notebooks for contribution and adding information for documentation
|
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[] | null | 2 | 2023-06-23T13:45:22 | 2023-07-12T20:25:34 | 2023-07-12T20:25:34 |
NONE
| null |
- Description: The documentation was ignored by the .gitignore and this allows contribution to the documentation by opening the documentation by unignoring the notebook files.
- Issue: Closes #6356
- 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!
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
|
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| 1,771,365,975 |
PR_kwDOIPDwls5TvlCN
| 6,647 |
Preserved custom keys in RetrievalQA
|
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[] | null | 4 | 2023-06-23T12:20:13 | 2023-10-14T14:38:02 | 2023-10-14T14:38:02 |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Tag maintainer: @rlancemartin, @eyurtsev,
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
# Description
Currently `RetrievalQA` drops any extra keys in the prompt apart from the memory and user input. So if I want a prompt like this:
```python
"""You are a friendly bot talking to a human.
Use the following pieces of context to answer the users question.
----------------
Context relevant to the question is: \n{context}
----------------
Recent conversation history is: \n{history}
----------------
You also have the following information: \n{extra_context1}\n{extra_context2}
----------------
Human: {query}
AI:
"""
```
Here `context` is retrieved from the database, `history` comes from memory and `query` is the last input provided by the user. If some extra context has to be provided apart from conversation history and database, like `extra_context1` and `extra_context2`, `RetrievalQA` cannot currently handle it.
With this change one can simply provide extra keys in in the `.run()` function of `RetrievalQA`. Like so:
```python
qa = KwargsRetrievalQA.from_chain_type(
llm=ChatOpenAI(),
chain_type='stuff',
retriever=retriever,
verbose=False,
chain_type_kwargs={
"verbose": True,
"prompt": prompt_template,
"memory": memory,
}
)
inp = input("Human: ")
query = inp
response = qa.run(
query=query,
history=memory,
extra_context1=extra_context1,
extra_context2=extra_context2,
)
```
# Maintainer
@rlancemartin
|
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| 1,771,335,088 |
PR_kwDOIPDwls5TveQn
| 6,645 |
adds doc_content_chars_max argument to WikipediaLoader
|
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[] | null | 2 | 2023-06-23T11:57:51 | 2023-06-26T12:59:32 | 2023-06-23T22:22:10 |
CONTRIBUTOR
| null |
# Description
It adds a new initialization param in `WikipediaLoader` so we can override the `doc_content_chars_max` param used in `WikipediaAPIWrapper` under the hood, e.g:
```python
from langchain.document_loaders import WikipediaLoader
# doc_content_chars_max is the new init param
loader = WikipediaLoader(query="python", doc_content_chars_max=90000)
```
## Decisions
`doc_content_chars_max` default value will be 4000, because it's the current value
I have added pycode comments
# Issue
#6639
# Dependencies
None
# Mantainer
@rlancemartin, @eyurtsev
# Twitter handle
[@elafo](https://twitter.com/elafo)
|
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| 1,771,313,441 |
PR_kwDOIPDwls5TvZld
| 6,644 |
Changed generate_prompt.py
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[] | null | 2 | 2023-06-23T11:40:18 | 2023-06-23T22:48:34 | 2023-06-23T22:48:34 |
CONTRIBUTOR
| null |
Modified regex for Fix: ValueError: Could not parse output
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: ValueError: Could not parse output: RegexParser(regex='QUESTION: (.*?)\\nANSWER: (.*)', output_keys=['query', 'answer'], default_output_key=None)
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @dev2049
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @dev2049
- Memory: @hwchase17
- Agents / Tools / Toolkits: @vowelparrot
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
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I_kwDOIPDwls5pk97G
| 6,643 |
Installation Issue with Langchain Package - 'predict_messages' Function Not Available in Pip Version 0.0.209
|
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[] | null | 2 | 2023-06-23T11:27:58 | 2023-10-01T16:05:48 | 2023-10-01T16:05:48 |
NONE
| null |
### System Info
Python 3.11.3
MacOs
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I encountered a problem during my initial installation of the Langchain package. I adhered to the installation instructions provided at https://python.langchain.com/docs/get_started/installation.
The command I used for installation was pip install langchain, which resulted in the installation of Langchain version 0.0.209.
However, when I attempted to execute the following code:
```
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
chat = ChatOpenAI()
res = chat.predict_messages([HumanMessage(
content="Translate this sentence from English to French. I love programming.")])
print(res.content)
```
I received an error message stating that the `predict_messages` function was not available. It appears that the package version available on pip does not align with the latest version on the GitHub repository.
Interestingly, when I installed the package from the cloned repository, it worked as expected.
### Expected behavior
After installing the Langchain package using pip install langchain, I should be able to import the OpenAI module from `langchain.chat_models` and use the predict function without any issues. The `predict_messages` function should be available and functional in the pip version of the package, just as it is in the version available in the GitHub repository.
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