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PR_kwDOIPDwls5ZO5yW
| 10,046 |
Feature/add sqlite vss
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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I_kwDOIPDwls5vx_di
| 10,045 |
`create_pandas_dataframe_agent` does not pass the `handle_parsing_error` variable into the underlying Agent.
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[] | null | 6 | 2023-08-31T11:53:16 | 2023-09-03T21:31:02 | 2023-09-03T21:31:01 |
NONE
| null |
### System Info
Python Version: 3.11.4 (main, Jul 5 2023, 08:40:20) [Clang 14.0.6 ]
Langchain Version: 0.0.273
Jupyter Notebook Version: 5.3.0
### Who can help?
@hwchase17
@agola
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behaviour:
1. Create a function to be used
```
def _handle_error(error) -> str:
return str(error)[:50]
```
2. Run `create_pandas_dataframe_agent`, pass in `handle_parsing_errors=_handle_error`
### Expected behavior
I expect an output similar to the last section of https://python.langchain.com/docs/modules/agents/how_to/handle_parsing_errors, but instead I still get the normal OutputParserError
|
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I_kwDOIPDwls5vxyj_
| 10,044 |
Getting invalid URL post after updating langchain from 0.0.200 to 0.0.267
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[] | null | 2 | 2023-08-31T11:17:31 | 2023-12-07T16:05:30 | 2023-12-07T16:05:29 |
NONE
| null |
### System Info
`langchain` - 0.0.267
`openai` - 0.27.7
MacOS 13.4.1 (22F82)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
This code was working when I used `langchain 0.0.200` (The call is to `AzureOpenAI` endpoint)
```python
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name=model_config.model_engine,
deployment_id=model_config.model_engine,
temperature=model_config.temperature,
max_tokens=model_config.max_tokens_for_request,
top_p=model_config.top_p,
openai_api_key=endpoint_config.api_key.secret,
api_base=endpoint_config.api_base,
api_type=endpoint_config.api_type,
api_version=endpoint_config.api_version)
chat_prompt = [SystemMessage(...), HumanMessage(...)]
response = llm(chat_prompt)
```
Once I updated `langchain` to `0.0.267`, I'm getting this error
```shell
openai.error.InvalidRequestError: Invalid URL (POST /v1/openai/deployments/gpt-35-turbo/chat/completions)
```
The endpoint itself is working (once I reverted back to `0.0.200` all started to work again)
### Expected behavior
I except to get an answer from the LLM
|
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PR_kwDOIPDwls5ZOT-B
| 10,041 |
fix wrong import line in cassandra doc page for vector store
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[] | null | 2 | 2023-08-31T10:33:30 | 2023-09-01T12:53:08 | 2023-08-31T23:05:46 |
CONTRIBUTOR
| null |
This fixes the exampe import line in the general "cassandra" doc page mdx file. (it was erroneously a copy of the chat message history import statement found below).
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I_kwDOIPDwls5vxYgX
| 10,040 |
Function calling use the wrong context
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[] | null | 4 | 2023-08-31T10:12:09 | 2023-12-07T16:05:35 | 2023-12-07T16:05:34 |
NONE
| null |
### Issue you'd like to raise.
I'm using function calls to obtain and process data. I have two classes designed as follows:
```python
class GetDataInput:
user_input: str = Field(description="User input")
class ProcessDataInput:
data_url: str = Field(description="Link to the data")
```
Moreover, I'm employing the `ConversationBufferWindowMemory`.
While the initial question posed to the bot yields the expected response, I encounter an issue when asking a subsequent question. Occasionally, instead of fetching new data, the bot utilizes the previous `data_url`.
How can I effectively manage this kind of situation?
Thank you for your assistance.
### Suggestion:
_No response_
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PR_kwDOIPDwls5ZN_BA
| 10,039 |
Impl chat message prompt type/dict (#10033)
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[] | null | 3 | 2023-08-31T09:35:45 | 2023-09-01T00:12:45 | 2023-09-01T00:12:44 |
CONTRIBUTOR
| null |
## Description
Dumping BaseMessagePromptTemplate to json and loading to original Template is difficult, as retrieving exact type of message type from json is not possible.
Therefore, I suggest adding _msg_type / _type property on BaseMessagePromptTemplate and utilized it on _dict and added load_message_prompt,
like In [BasePromptTemplate dict method](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L108-L116).
## Issue
#10033
## Dependencies
None
|
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[] | null | 2 | 2023-08-31T09:27:42 | 2023-12-07T16:05:40 | 2023-12-07T16:05:39 |
NONE
| null |
### Issue you'd like to raise.
is this implemented?
https://github.com/langchain-ai/langchain/pull/1222/commits
### Suggestion:
_No response_
|
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| 1,875,107,736 |
I_kwDOIPDwls5vw9-Y
| 10,037 |
Problems with using LangChain Sagemaker Embedding for Llama 2.0 Inference Endpoint in Sagemaker
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[] | null | 2 | 2023-08-31T09:09:17 | 2023-08-31T11:11:55 | 2023-08-31T11:11:55 |
NONE
| null |
### Issue you'd like to raise.
Hi all😊 ,
I work in a network-isolated SageMaker environment. There I have hosted a llama 2.0 -7b chat Inference Endpoint (from [HF ](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf))
For a project I would like to work with LangChain.
Currently I am failing with the embeddings.
I can't use the HF embedding engine because of the network isolation.
hf_embedding = HuggingFaceInstructEmbeddings()
Alternatively I found a SageMaker embedding:
https://python.langchain.com/docs/integrations/text_embedding/sagemaker-endpoint
I used the same code from the docu:
```python
from typing import Dict, List
from langchain.embeddings import SagemakerEndpointEmbeddings
from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler
import json
class ContentHandler(EmbeddingsContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:
"""
Transforms the input into bytes that can be consumed by SageMaker endpoint.
Args:
inputs: List of input strings.
model_kwargs: Additional keyword arguments to be passed to the endpoint.
Returns:
The transformed bytes input.
"""
# Example: inference.py expects a JSON string with a "inputs" key:
input_str = json.dumps({"inputs": inputs, **model_kwargs})
return input_str.encode("utf-8")
def transform_output(self, output: bytes) -> List[List[float]]:
"""
Transforms the bytes output from the endpoint into a list of embeddings.
Args:
output: The bytes output from SageMaker endpoint.
Returns:
The transformed output - list of embeddings
Note:
The length of the outer list is the number of input strings.
The length of the inner lists is the embedding dimension.
"""
# Example: inference.py returns a JSON string with the list of
# embeddings in a "vectors" key:
response_json = json.loads(output.read().decode("utf-8"))
return response_json["vectors"]`
content_handler = ContentHandler()
embeddings = SagemakerEndpointEmbeddings(
endpoint_name=ENDPOINT_NAME,
region_name=REGION_NAME,
content_handler=content_handler ,
)
query_result = embeddings.embed_query("foo")
```
But i get the following error:
`---------------------------------------------------------------------------
ModelError Traceback (most recent call last)
File /opt/conda/lib/python3.10/site-packages/langchain/embeddings/sagemaker_endpoint.py:153, in SagemakerEndpointEmbeddings._embedding_func(self, texts)
[...]
ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (422) from primary with message "Failed to deserialize the JSON body into the target type: inputs: invalid type: sequence, expected a string at line 1 column 11". See https://eu-central-1.console.aws.amazon.com/cloudwatch/home?xxxxx in account xxxxxx for more information.`
### Suggestion:
_No response_
|
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| 1,875,045,957 |
I_kwDOIPDwls5vwu5F
| 10,035 |
Conversational Retrieval Chain.from_llm integration with router chains
|
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[] | null | 2 | 2023-08-31T08:32:26 | 2023-12-07T16:05:44 | 2023-12-07T16:05:43 |
NONE
| null |
### Issue you'd like to raise.
The idea is, that I have a vector store with a conversational retrieval chain.from_llm, and I want to create other functions such as send an email, etc. so that when a user queries for something, it determines if it should use the Conv retrieval chain or the other functions such as sending an email function, and it seems I need to use the router to achieve this, but when I do, I get a lot of errors such as
```
"default_chain -> prompt
field required (type=value_error.missing)
default_chain -> llm
field required (type=value_error.missing)
default_chain -> combine_docs_chain
extra fields not permitted (type=value_error.extra)
```
How do i integrate with conv retrieval chain with routers to achieve my purpose, the only examples i have seen do not use conv retrieval chain
### Suggestion:
_No response_
|
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| 1,875,031,907 |
I_kwDOIPDwls5vwrdj
| 10,034 |
context to pydantic_object
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[] | null | 2 | 2023-08-31T08:23:33 | 2023-12-07T16:05:50 | 2023-12-07T16:05:49 |
NONE
| null |
### Feature request
while parsing LLM output with `pydantic_object` it would be nice to send to the `parse` function `context` object , like `pydantic` docs does here: https://docs.pydantic.dev/latest/usage/validators/#validation-context
### Motivation
In order to do validation on LLM output rather than just parse it, in some cases to do the validation we need some context.
### Your contribution
This change may require to support `pydantic` v2 not sure about backword capabilities.
in `PydanticOutputParser.parse` function insted of `parse_obj` we should use `model_validate` then we could send context object(it can be optional) to `model_validate`
from pydantic:
```python
@typing_extensions.deprecated(
'The `parse_obj` method is deprecated; use `model_validate` instead.', category=PydanticDeprecatedSince20
)
def parse_obj(cls: type[Model], obj: Any) -> Model: # noqa: D102
warnings.warn('The `parse_obj` method is deprecated; use `model_validate` instead.', DeprecationWarning)
return cls.model_validate(obj)
```
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| 10,033 |
Add message type property method on BaseMessagePromptTemplate
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[] | null | 1 | 2023-08-31T08:23:10 | 2023-09-01T00:13:23 | 2023-09-01T00:13:23 |
CONTRIBUTOR
| null |
### Feature request
I'm utilizing BaseMessagePromptTemplate with external DBs, for version control.
However, dumping BaseMessagePromptTemplate to json and loading to original Template is difficult, as retrieving exact type of message type from json is not possible.
Therefore, it would be useful to add _msg_type property on BaseMessagePromptTemplate,
like In [BasePromptTemplate dict method](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/schema/prompt_template.py#L108-L116).
### Motivation
Dumping ChatPromptTemplate to json and loading to original Template is difficult,
as retrieving exact type of MessageLike (Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate]) property is difficult.
### Your contribution
If you allow me, I'd like to make a pull request regarding to this.
|
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| 1,875,002,896 |
I_kwDOIPDwls5vwkYQ
| 10,031 |
Dynamic "search_kwargs" during RetrievalQAWithSourcesChain call
|
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[] | null | 2 | 2023-08-31T08:06:10 | 2023-12-05T17:47:16 | 2023-12-05T17:47:08 |
NONE
| null |
### Feature request
When using `RetrievalQAWithSourcesChain`, the chain call can't accept `search_kwargs` to pass then to the retriever like this:
```python
response = chain({"question": query ,'search_kwargs': search_kwargs})
```
In particular, I tried with `Milvus` and `MilvusRetriever` and natively could't find a way.
### Motivation
Instead to create the same chain but with different `search_kwargs` to be sended to the retriever, it would be usefull to allow [optionally] the search_kwargs sendeed dynamically in the call.
### Your contribution
I could allow this behaevoir with the following modification:
Adding a `search_kwargs_key` to take `search_kwargs` that then will be sended to self.retriever.get_relevant_documents( ..., `**search_kwargs`,...).
```python
class customRetrievalQAWithSourcesChain(RetrievalQAWithSourcesChain):
search_kwargs_key:str = "search_kwargs"
def _get_docs(
self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun
) -> List[Document]:
question = inputs[self.question_key]
search_kwargs = inputs[self.search_kwargs_key]
docs = self.retriever.get_relevant_documents(
question, **search_kwargs, callbacks=run_manager.get_child()
)
return self._reduce_tokens_below_limit(docs)
async def _aget_docs(
self, inputs: Dict[str, Any], *, run_manager: AsyncCallbackManagerForChainRun
) -> List[Document]:
question = inputs[self.question_key]
search_kwargs = inputs[self.search_kwargs_key]
docs = await self.retriever.aget_relevant_documents(
question,**search_kwargs, callbacks=run_manager.get_child()
)
return self._reduce_tokens_below_limit(docs)
```
And finally allowing to VectorStoreRetriever take `**search_kwargs` instead of `self.search_kwargs`
```python
class customRetriever(VectorStoreRetriever):
def _get_relevant_documents(
self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **search_kwargs: Any,
) -> List[Document]:
if self.search_type == "similarity":
docs = self.vectorstore.similarity_search(query, **search_kwargs)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
self.vectorstore.similarity_search_with_relevance_scores(
query, **search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = self.vectorstore.max_marginal_relevance_search(
query, **search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
async def _aget_relevant_documents(
self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun, **search_kwargs: Any,
) -> List[Document]:
if self.search_type == "similarity":
docs = await self.vectorstore.asimilarity_search(
query, **search_kwargs
)
elif self.search_type == "similarity_score_threshold":
docs_and_similarities = (
await self.vectorstore.asimilarity_search_with_relevance_scores(
query, **search_kwargs
)
)
docs = [doc for doc, _ in docs_and_similarities]
elif self.search_type == "mmr":
docs = await self.vectorstore.amax_marginal_relevance_search(
query, **search_kwargs
)
else:
raise ValueError(f"search_type of {self.search_type} not allowed.")
return docs
```
If you consider this usefull I could open a PR (confirm please). I not, maybe someone can find this usefull.
Best regards,
|
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| 1,874,898,180 |
PR_kwDOIPDwls5ZNICl
| 10,029 |
Add AliCloudPaiEas class in langchain.llms to access alicloud pai-eas service.
|
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[] | null | 1 | 2023-08-31T06:54:43 | 2023-08-31T07:17:01 | 2023-08-31T07:17:01 |
CONTRIBUTOR
| null |
### Description
This PR aims at adding new llm type AliCloudPaiEAS to support service accessing for alicloud product named pai-eas service. PAI-EAS can help users to deploy llm models on alicloud.
### Usage
The user needs to start an eas service on alicloud and then set the service's access url and token as a params for AliCloudPaiEAS. Then AliCloudPaiEAS object as a llm object is passed to the Chain to run an output.
The usage is like:
`llm = AliCloudPaiEAS(eas_service_url="your_access_url",eas_service_token="your_access_token"`
`llm_chain = LLMChain(prompt=prompt, llm=llm)
`
`llm_chain.run("Hello there.")`
### Test and example
The integration test case is located at libs/langchain/tests/integration_tests/llms/test_alicloud_eas.py
The example notebook is located at docs/extras/integrations/lls/alicloud_api_eas.ipynb
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I_kwDOIPDwls5vwIhJ
| 10,028 |
Does anyone created agent with tool which takes "dataframe" and "user_input" as input variables in langchain?
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[] | null | 3 | 2023-08-31T06:46:58 | 2023-12-07T16:05:55 | 2023-12-07T16:05:54 |
NONE
| null |
### Issue you'd like to raise.
Does anyone created agent with tool which takes "dataframe" and "user_input" as input variables in langchain?
I do not want use dataframe agent which is there already in langchain. As I need pass further instruction in the prompt template.
### Suggestion:
_No response_
|
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| 1,874,874,756 |
I_kwDOIPDwls5vwFGE
| 10,027 |
Issue: I dont know what the meaning of OPENAI_API_BASE and OPENAI_PROXY in ChatOpenAI
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[] | null | 1 | 2023-08-31T06:35:39 | 2023-08-31T08:32:27 | 2023-08-31T08:32:27 |
NONE
| null |
Can anyone tell me the difference between these two parameters? When setting up the ChatOpenAI model
|
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I_kwDOIPDwls5vv3qt
| 10,025 |
Issue: AttributeError: 'list' object has no attribute 'embedding'
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[] | null | 2 | 2023-08-31T05:40:08 | 2023-12-07T16:06:00 | 2023-12-07T16:05:59 |
NONE
| null |
### Issue you'd like to raise.
Hello,
I am facing some issues with the code I am trying to run as it did run perfectly up until recently when an Error started being thrown out as AttributeError: 'list' object has no attribute 'embedding'. Below is the traceback if the error. Please do let me know if some code snippet excerpts will also be needed to facilitate debugging the code.
Traceback:
File "/home/ataliba/llm/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/home/ataliba/LLM_Workshop/Experimental_Lama_QA_Retrieval/Andro_GPT_Llama2.py", line 268, in <module>
response = qa_chain.run(user_query, callbacks=[cb])
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 481, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 288, in __call__
raise e
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/chains/base.py", line 282, in __call__
self._call(inputs, run_manager=run_manager)
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/chains/conversational_retrieval/base.py", line 134, in _call
docs = self._get_docs(new_question, inputs, run_manager=_run_manager)
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/chains/conversational_retrieval/base.py", line 286, in _get_docs
docs = self.retriever.get_relevant_documents(
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/schema/retriever.py", line 208, in get_relevant_documents
raise e
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/schema/retriever.py", line 201, in get_relevant_documents
result = self._get_relevant_documents(
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/vectorstores/base.py", line 571, in _get_relevant_documents
docs = self.vectorstore.max_marginal_relevance_search(
File "/home/ataliba/llm/lib/python3.10/site-packages/langchain/vectorstores/docarray/base.py", line 197, in max_marginal_relevance_search
np.array(query_embedding), docs.embedding, k=k
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5ZM20G
| 10,024 |
add bloomz_7b, llama-2-7b, llama-2-13b, llama-2-70b to ErnieBotChat
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[] | null | 1 | 2023-08-31T05:37:31 | 2023-08-31T07:38:56 | 2023-08-31T07:38:55 |
CONTRIBUTOR
| null |
- Description: Add bloomz_7b, llama-2-7b, llama-2-13b, llama-2-70b to ErnieBotChat, which only supported ERNIE-Bot-turbo and ERNIE-Bot.
- Issue: #10022,
- Dependencies: no extra dependencies
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| 1,874,814,732 |
PR_kwDOIPDwls5ZM2No
| 10,023 |
Fix link in Neo4j provider page
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[] | null | 1 | 2023-08-31T05:34:02 | 2023-08-31T07:32:42 | 2023-08-31T07:32:42 |
CONTRIBUTOR
| null | null |
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I_kwDOIPDwls5vv0Jg
| 10,022 |
Support more models for ErnieBotChat
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[] | null | 3 | 2023-08-31T05:22:17 | 2023-12-13T16:07:03 | 2023-12-13T16:07:02 |
CONTRIBUTOR
| null |
### Feature request
The class ErnieBotChat defined in libs/langchain/langchain/chat_models/ernie.py only supports 2 models, which are ERNIE-Bot-turbo and ERNIE-Bot. While a bunch of new models are supported by BCE (Baidu Cloud Engine), such as llama-2-7b chat (https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Rlki1zlai). It would be better that this class supported more models.
### Motivation
When I was using ErnieBotChat, I've found that ErnieBotChat cannot recognize models other than ERNIE-Bot-turbo and ERNIE-Bot. Instead, it raised an error "Got unknown model_name {self.model_name}".
### Your contribution
If possible, I would be happy to help resolve this issue. I plan to add all models BCE (Baidu Cloud Engine) supports at this time(2023-8-31). The fixes will be simple, just add more cases around line 106 in the file libs/langchain/langchain/chat_models/ernie.py from the master branch.
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PR_kwDOIPDwls5ZMz3U
| 10,021 |
Add integration TCs on bing search (#8068)
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[] | null | 1 | 2023-08-31T05:20:00 | 2023-08-31T07:34:07 | 2023-08-31T07:34:07 |
CONTRIBUTOR
| null |
## Description
Added integration TCs on bing search utility
## Issue
#8068
## Dependencies
None
|
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PR_kwDOIPDwls5ZMwog
| 10,020 |
Fix Typo : `openai_api_key` -> `serpapi_api_key`
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[] | null | 1 | 2023-08-31T04:59:56 | 2023-09-04T03:46:52 | 2023-08-31T07:33:13 |
CONTRIBUTOR
| null |
Fixed typo in the comments Notebook. (which says `openai_api_key` for SerpAPI)
|
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I_kwDOIPDwls5vvfMr
| 10,019 |
fix: Loading documents from a Youtube Url
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[] | null | 1 | 2023-08-31T03:19:25 | 2023-12-07T16:06:10 | 2023-12-07T16:06:09 |
NONE
| null |
### System Info
MacOS M2 13.4.1 (22F82)
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce behaviour:
1. Run the [tutorial](https://python.langchain.com/docs/integrations/document_loaders/youtube_audio) with the default parameters `save_dir = "~/Downloads/YouTube"`
2. After calling `docs = loader.load()` the docs will be empty
I have implemented a dummy fix for the interim.
The error is here in this file: from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
`YouTubeAudioLoader.yield_blobs` method
loader = FileSystemBlobLoader(self.save_dir, glob="*.m4a")
```
# This doesn't always work (MacOS)
loader = FileSystemBlobLoader(self.save_dir, glob="*.m4a")
```
The reason it doesn't work is that it's trying to use ~/Downloads/YouTube.
The fix I propose is either:
- Use the FULL file path in `save_dir` in the tutorial.
- Replace the problematic line with this, so that it finds the actual directory, even if you prefer to use `~` for specifying file paths.
```
loader = FileSystemBlobLoader(os.path.expanduser(self.save_dir), glob="*.m4a")
```
### Expected behavior
There should be documents in the loader.load() variable.
### My Fix
```
# Yield the written blobs
"""
you could fix save_dir like this...
(old)
save_dir = "~/Downloads/YouTube"
(new)
"/Users/shawnesquivel/Downloads/YouTube"
"""
# This doesn't always work (MacOS)
loader = FileSystemBlobLoader(self.save_dir, glob="*.m4a")
# This works
loader = FileSystemBlobLoader(os.path.expanduser(self.save_dir), glob="*.m4a")
```
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@dosu-bot
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[] | null | 7 | 2023-08-31T03:01:34 | 2023-12-07T16:06:15 | 2023-12-07T16:06:14 |
NONE
| null |
@dosu-bot
The issue is that the "string indices must be indices", but your transformation does not deal with that. Here is the curretn code, please see below and change to avoid this error:
```from langchain.docstore.document import Document
from typing import Dict
from langchain import PromptTemplate, SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain.chains.question_answering import load_qa_chain
import json
example_doc_1 = """
string
"""
docs = [
Document(
page_content=example_doc_1,
)
]
query = """
prompt
"""
prompt_template = """Use the following pieces of context to answer the question at the end.
{context}
Question: {question}
Answer:"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
class ContentHandler(LLMContentHandler):
content_type = "application/json"
accepts = "application/json"
def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:
input_dict = {"inputs": prompt, "parameters": model_kwargs}
return json.dumps(input_dict).encode('utf-8')
def transform_output(self, output: bytes) -> str:
response_json = json.loads(output.read().decode("utf-8"))
print("output: ", response_json[0])
return response_json[0]["generation"]
content_handler = ContentHandler()
chain = load_qa_chain(
llm=SagemakerEndpoint(
endpoint_name="endpointname",
credentials_profile_name="profilename",
region_name="us-east-1",
model_kwargs={"temperature": 1e-10, "max_length":500},
endpoint_kwargs={"CustomAttributes": "accept_eula=true"},
content_handler=content_handler,
),
prompt=PROMPT,
)
chain({"input_documents": docs, "question": query}, return_only_outputs=True)```
_Originally posted by @maggarwal25 in https://github.com/langchain-ai/langchain/issues/10012#issuecomment-1700300547_
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| 1,874,701,323 |
PR_kwDOIPDwls5ZMeKO
| 10,016 |
fix scoring profile example
|
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| false | null |
[] | null | 1 | 2023-08-31T02:52:05 | 2023-08-31T07:35:06 | 2023-08-31T07:35:06 |
CONTRIBUTOR
| null |
- Description: A change in the documentation example for Azure Cognitive Vector Search with Scoring Profile so the example works as written
- Issue: #10015
- Dependencies: None
- Tag maintainer: @baskaryan @ruoccofabrizio
- Twitter handle: @poshporcupine
|
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| 1,874,685,238 |
I_kwDOIPDwls5vvW02
| 10,015 |
Azure Cognitive Search Scoring Profile does not work as documented
|
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[] | null | 5 | 2023-08-31T02:30:27 | 2023-12-08T16:05:06 | 2023-12-08T16:05:05 |
CONTRIBUTOR
| null |
### System Info
langchain 0.0.277
azure-search-documents 11.4.0b8
python 3.1.0.11
### Who can help?
@baskaryan
@ruoccofabrizio
### 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
Repro steps:
1. I took the code in https://github.com/langchain-ai/langchain/blob/master/docs/extras/integrations/vectorstores/azuresearch.ipynb and placed it in a python file and ran the chunk under "Create a new index with a Scoring profile"
2. I executed the code and got the following scores:
3. I went into the Azure Search Index and adjusted the "scoring_profile" to have a boost of 1000 instead of 100 and got the exact same scores.
### Expected behavior
I expected all of the score to be 10 times larger than the scores I got. After much experimentation I do not believe that scoring profiles would with a vector search if a search term is specified. If "None" is specified the behavior is correct. Change the last line of the example to:
`
res = vector_store.similarity_search(query="Test 1", k=3, search_type="similarity")`
And the results respect the Scoring profile and behave as expected when the scoring profile is changed.
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PR_kwDOIPDwls5ZMO88
| 10,014 |
`Yahoo Finance News` tool
|
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[] | null | 1 | 2023-08-31T01:55:26 | 2023-09-04T16:52:27 | 2023-09-03T21:43:57 |
COLLABORATOR
| null |
Added:
- the `Yahoo Finance News` tool
- Ut-s
- An example
|
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PR_kwDOIPDwls5ZL6_F
| 10,013 |
refactor: rename to OpaquePrompts
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[] | null | 1 | 2023-08-31T00:10:20 | 2023-08-31T21:55:35 | 2023-08-31T19:21:25 |
CONTRIBUTOR
| null |
Renamed to OpaquePrompts
cc @baskaryan Thanks in advance!
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I_kwDOIPDwls5vujy_
| 10,012 |
Issues with llama-2-7b-f
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[] | null | 10 | 2023-08-30T22:43:26 | 2023-11-30T13:20:12 | null |
NONE
| null |
### System Info
latest versions for python and langchain
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I used the demo code to run Sagemaker using Langchain for llama-2-7b-f
However, I'm getting the following issues:
```
ValueError: Error raised by inference endpoint: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (424) from primary with message "{
"code":424,
"message":"prediction failure",
"error":"string indices must be integers"
}
```
This is what is shown from AWS Logs:
`[INFO ] PyProcess - W-80-model-stdout: [1,0]<stdout>:TypeError: string indices must be integers`
How do I resolve this
### Expected behavior
Shown:
```
ValueError: Error raised by inference endpoint: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (424) from primary with message "{
"code":424,
"message":"prediction failure",
"error":"string indices must be integers"
}
```
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| 1,874,448,914 |
I_kwDOIPDwls5vudIS
| 10,011 |
No support for ConversationalRetrievalChain with passing in memory and system message
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[] | null | 6 | 2023-08-30T22:14:38 | 2023-12-27T16:01:59 | null |
NONE
| null |
### System Info
The latest langchain version
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have written a script that initializes a chat which has access to a faiss index. I want to pass in a system prompt to the conversational agent. How do I add a system prompt to the conversational agent while making sure that the chat history is passed in and not stored and updated in memory. The code below shows details
```
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import AzureChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.vectorstores import FAISS
from dotenv import load_dotenv
from langchain.schema import SystemMessage
load_dotenv()
import os
system_message = SystemMessage(
content="You are an AI that always writes text backwards e.g. 'hello' becomes 'olleh'."
)
embeddings = OpenAIEmbeddings(deployment="embeddings",
temperature=0,
model="text-embedding-ada-002",
openai_api_type="azure",
openai_api_version="2023-03-15-preview",
openai_api_base="https://azcore-kusto-bot-openai.openai.azure.com/",
openai_api_key=os.getenv("OPENAI_API_KEY"), chunk_size = 1)
vectorstore = FAISS.load_local("faiss_index", embeddings)
prompt = "Your name is AzCoreKustoCopilot"
llm=AzureChatOpenAI(deployment_name="16k-gpt-35",
model="gpt-35-turbo-16k",
openai_api_type="azure",
openai_api_version="2023-03-15-preview",
openai_api_base="https://azcore-kusto-bot-openai.openai.azure.com/",
openai_api_key=os.getenv("OPENAI_API_KEY"))
retriever = vectorstore.as_retriever()
def initialize_chat():
# retriever = vectorstore.as_retriever()
chat = ConversationalRetrievalChain.from_llm(llm, retriever=retriever)
print('this is chat', chat)
return chat
def answer_query(chat, user_query, chat_history):
"""
user_query is the question
chat_history is a list of lists: [[prev_prev_query, prev_prev_response], [prev_query, prev_response]]
we convert this into a list of tuples
"""
chat_history_tups = [tuple(el) for el in chat_history]
print(f"user_query:{user_query}, chat_history: {chat_history_tups}")
result = chat({"question":user_query, "chat_history": chat_history_tups})
return result["answer"]
```
### Expected behavior
There is no way to pass in a system message and chat history to a ConversationalRetrievalChain. Why is that?
|
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| 1,874,332,785 |
PR_kwDOIPDwls5ZLNe-
| 10,007 |
Bagatur/dereference tool schema
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[] | null | 1 | 2023-08-30T20:38:16 | 2023-08-31T07:48:13 | 2023-08-31T07:48:12 |
COLLABORATOR
| null |
fix for #9375
|
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PR_kwDOIPDwls5ZLNZH
| 10,006 |
Add ne comparator
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[] | null | 1 | 2023-08-30T20:37:55 | 2023-09-03T21:45:11 | 2023-09-03T21:45:11 |
CONTRIBUTOR
| null |
Description: Adds the not comparator and operator to pinecone, chroma and deeplake.
Issue: Not a registered issue but when using a selfqueryretriever with pinecone I got this error + stacktrace when I entered a query that asked to not include specific data:
> raised following `error:`
> Received unrecognized function ne. Valid functions are [<Operator.AND: 'and'>, <Operator.OR: 'or'>, <Operator.NOT: 'not'>, <Comparator.EQ: 'eq'>, <Comparator.GT: 'gt'>, <Comparator.GTE: 'gte'>, <Comparator.LT: 'lt'>, <Comparator.LTE: 'lte'>]
I noticed that chroma and deeplake also support not equals/not filtering so I added it there as well
[pinecone](https://docs.pinecone.io/docs/metadata-filtering#metadata-query-language)
[chroma](https://docs.trychroma.com/usage-guide#filtering-by-metadata)
[deeplake](https://docs.activeloop.ai/enterprise-features/compute-engine/querying-datasets/query-syntax#and-or-not)
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PR_kwDOIPDwls5ZKkxD
| 10,005 |
guides docs nits
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| 1,874,126,698 |
PR_kwDOIPDwls5ZKg0q
| 10,004 |
rename local llm guide
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| 1,874,114,185 |
PR_kwDOIPDwls5ZKeHs
| 10,003 |
make privacy guide section
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| 1,874,096,952 |
PR_kwDOIPDwls5ZKaX7
| 10,002 |
update moderation docs
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| 1,874,063,727 |
PR_kwDOIPDwls5ZKTIU
| 10,001 |
docs(text_splitter): update document of character splitter with tiktoken
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[] | null | 1 | 2023-08-30T17:04:17 | 2023-09-03T21:45:46 | 2023-09-03T21:45:45 |
CONTRIBUTOR
| null |
The current document has not mentioned that splits larger than chunk size would happen. I update the related document and explain why it happens and how to solve it.
related issue #1349 #3838 #2140
|
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PR_kwDOIPDwls5ZKQHR
| 10,000 |
docs: `google-drive-tool` example fix
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[] | null | 4 | 2023-08-30T16:53:27 | 2023-09-01T20:45:10 | 2023-09-01T20:31:26 |
COLLABORATOR
| null |
This notebook was mistakenly placed in the `toolkits` folder and appears within `Agents & Toolkits` menu. But it should be in `Tools`.
Moved example into `tools/`; updated title to consistent format.
|
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PR_kwDOIPDwls5ZKDte
| 9,999 |
Google drive integration (lite)
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[] | null | 1 | 2023-08-30T16:14:50 | 2023-09-03T22:54:42 | 2023-09-03T22:54:42 |
CONTRIBUTOR
| null |
My other [pull-request](https://github.com/langchain-ai/langchain/pull/5135) is too big to be acceptable.
I propose another 'lite' version.
I update only notebook to propose an integration with the external project [`langchain-googledrive`](https://github.com/pprados/langchain-googledrive).
|
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PR_kwDOIPDwls5ZKApb
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Buckler/dynamic retriever parameters
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[] | null | 4 | 2023-08-30T16:05:38 | 2023-09-05T09:06:39 | 2023-09-03T21:57:11 |
CONTRIBUTOR
| null |
Description: Enhance kwargs Support in Internal Retriever Method for EnsembleRetriever and MergerRetriever Classes
This pull request introduces a valuable enhancement to the EnsembleRetriever and MergerRetriever classes by allowing the seamless flow of keyword arguments (**kwargs) to the internal _get_relevant_document method. This improvement empowers these classes with greater flexibility and customization when invoking the _get_relevant_document method.
Changes Made:
Previously, the internal _get_relevant_document method within the EnsembleRetriever and MergerRetriever classes did not directly receive keyword arguments. This enhancement addresses this limitation by enabling the propagation of **kwargs directly to the said method. This enables developers to conveniently pass additional arguments and options, extending the capabilities of these retriever classes.
Benefits:
Enhanced Flexibility: With this enhancement, users can now pass arbitrary keyword arguments when calling the _get_relevant_document method within the EnsembleRetriever and MergerRetriever classes. This facilitates customizations, configuration adjustments, and integration with other components in a more seamless manner.
- Tag maintainer: @baskaryan
- Twitter handle: Buckler89
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PR_kwDOIPDwls5ZJtK7
| 9,997 |
bump 277
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[] | null | 1 | 2023-08-30T15:13:27 | 2023-08-30T15:29:52 | 2023-08-30T15:29:51 |
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| null | null |
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I_kwDOIPDwls5vsNHq
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Solve the problem of working with incomplete document chunks and multiple documents
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| false | null |
[] | null | 7 | 2023-08-30T14:58:47 | 2023-12-08T17:30:51 | null |
CONTRIBUTOR
| null |
### Feature request
An agent for searching iteratively multiple documents without the problem of processing incomplete document chunks.
An option to include the metadata (source references) in the prompt.
### Motivation
Normally documents are split into chunks before being added to Chroma
When the data is queried, Chroma returns these chunks of incomplete documents and feed them to the prompt.
Thus, the LLM sometimes is not provided with the complete information and will fail to answer.
This is a big problem, especially when the split occurs in the middle of a list (eg: a text listing the 10 commandments of god).
The LLM won't have a chance to know they are 10.
Besides, LangChain "stuff" retriever is just mixing all these chunks together, without even separating them nor adding the documents metadata of each chunk. Mixing different sentences could confuse the LLM.
If this can be solved using document_prompt templates, this should be added to the documentation.
I would also expect to include document souces into the prompt, so the LLM and provide the used sources (not all sources retrieved by the Chroma query).
I blieve the queries should be processed by an agent with the ability to detect when there may be missing previous and/or following chunks in order to fetch them in a subsequent iteration if required.
### Your contribution
I can help coding and testing, but I need feedback for the design and to know which other existing componentes could/should be used.
|
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| 9,995 |
refactor(document_loaders): abstract page evaluation logic in PlaywrightURLLoader
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| false | null |
[] | null | 3 | 2023-08-30T14:47:30 | 2023-08-31T07:45:35 | 2023-08-31T07:45:34 |
COLLABORATOR
| null |
This PR brings structural updates to `PlaywrightURLLoader`, aiming at making the code more readable and extensible through the abstraction of page evaluation logic. These changes also align this implementation with a similar structure used in LangChain.js.
The key enhancements include:
1. Introduction of 'PlaywrightEvaluator', an abstract base class for all evaluators.
2. Creation of 'UnstructuredHtmlEvaluator', a concrete class implementing 'PlaywrightEvaluator', which uses `unstructured` library for processing page's HTML content.
3. Extension of 'PlaywrightURLLoader' constructor to optionally accept an evaluator of the type 'PlaywrightEvaluator'. It defaults to 'UnstructuredHtmlEvaluator' if no evaluator is provided.
4. Refactoring of 'load' and 'aload' methods to use the 'evaluate' and 'evaluate_async' methods of the provided 'PageEvaluator' for page content handling.
This update brings flexibility to 'PlaywrightURLLoader' as it can now utilize different evaluators for page processing depending on the requirement. The abstraction also improves code maintainability and readability.
Please find the reference for LangChain.js implementation [here](https://github.com/hwchase17/langchainjs/blob/29f3ffdcb34acce1f1db78078f4e7d184eaec6b5/langchain/src/document_loaders/web/playwright.ts#L59).
Maintainers for review: @rlancemartin, @eyurtsev
Twitter: @ywkim
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I_kwDOIPDwls5vrpHY
| 9,993 |
How to pass a custom prompt in graphQA or GraphCypherQA chain
|
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[] | null | 4 | 2023-08-30T13:42:34 | 2023-12-06T17:43:00 | 2023-12-06T17:42:59 |
NONE
| null |
### Feature request
```
from langchain.chat_models import ChatOpenAI
from langchain.chains import GraphCypherQAChain
from langchain.graphs import Neo4jGraph
graph = Neo4jGraph(
url="bolt://localhost:7687", username="neo4j", password="pleaseletmein"
)
chain = GraphCypherQAChain.from_llm(ChatOpenAI(temperature=0), graph=graph, verbose=True)
print(chain.run("Question to the graph"))
```
In the above Code, How can I pass my custom prompt as promptTemplate? Please give me an example
### Motivation
Custom prompt support in Knowledge Graph QA
### Your contribution
Custom prompt support in Knowledge Graph QA
|
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PR_kwDOIPDwls5ZIRSN
| 9,992 |
Extend the FalkorDB QA demo
|
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[] | null | 1 | 2023-08-30T11:20:04 | 2023-08-30T14:50:14 | 2023-08-30T14:13:18 |
CONTRIBUTOR
| null |
- Description: Extend the FalkorDB QA demo
- Tag maintainer: @baskaryan
|
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PR_kwDOIPDwls5ZHxHi
| 9,989 |
implement vectorstores by tencent vectordb
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[] | null | 2 | 2023-08-30T09:43:53 | 2023-08-31T07:54:15 | 2023-08-31T07:48:26 |
NONE
| null |
Hi there！
I'm excited to open this PR to add support for using 'Tencent Cloud VectorDB' as a vector store.
Tencent Cloud VectorDB is a fully-managed, self-developed, enterprise-level distributed database service designed for storing, retrieving, and analyzing multi-dimensional vector data. The database supports multiple index types and similarity calculation methods, with a single index supporting vector scales up to 1 billion and capable of handling millions of QPS with millisecond-level query latency. Tencent Cloud VectorDB not only provides external knowledge bases for large models to improve their accuracy, but also has wide applications in AI fields such as recommendation systems, NLP services, computer vision, and intelligent customer service.
The PR includes:
Implementation of Vectorstore.
I have read your [contributing guidelines](https://github.com/hwchase17/langchain/blob/72b7d76d79b0e187426787616d96257b64292119/.github/CONTRIBUTING.md). And I have passed the tests below
make format
make lint
make coverage
make test
|
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I_kwDOIPDwls5vpxk7
| 9,987 |
AzureMLOnlineEndpoint not working, 404 error, but same url and api key works with standard http
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[] | null | 5 | 2023-08-30T08:45:03 | 2023-11-03T14:14:44 | 2023-11-03T14:14:44 |
CONTRIBUTOR
| null |
### System Info
0.0.276
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have deployed llama2 in Azure ML endpoint and when I test it with langchain code I got a 404 Error. However using standard request library from python it works
Below : askdocuments2 ---> no lanchain
askdocuments --->langchain
same endpoint url, same key.
```
def askdocuments2(
question):
# Request data goes here
# The example below assumes JSON formatting which may be updated
# depending on the format your endpoint expects.
# More information can be found here:
# https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script
formatter = LlamaContentFormatter()
data = formatter.format_request_payload(prompt=question, model_kwargs={"temperature": 0.1, "max_tokens": 300})
body = data
url = 'https://llama-2-7b-test.westeurope.inference.ml.azure.com/score'
# Replace this with the primary/secondary key or AMLToken for the endpoint
api_key = ''
if not api_key:
raise Exception("A key should be provided to invoke the endpoint")
# The azureml-model-deployment header will force the request to go to a specific deployment.
# Remove this header to have the request observe the endpoint traffic rules
headers = {'Content-Type': 'application/json', 'Authorization': ('Bearer ' + api_key), 'azureml-model-deployment': 'llama'}
req = urllib.request.Request(url, body, headers)
try:
response = urllib.request.urlopen(req)
result = response.read()
decoded_data = json.loads(result.decode('utf-8'))
text = decoded_data[0]["0"]
return text
except urllib.error.HTTPError as error:
print("The request failed with status code: " + str(error.code))
# Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure
print(error.info())
print(error.read().decode("utf8", 'ignore'))
def askdocuments(
question):
try:
content_formatter = LlamaContentFormatter()
llm = AzureMLOnlineEndpoint(
endpoint_api_key="",
deployment_name="llama-2-7b-test",
endpoint_url="https://llama-2-7b-test.westeurope.inference.ml.azure.com/score",
model_kwargs={"temperature": 0.8, "max_tokens": 300},
content_formatter=content_formatter
)
formatter_template = "Write a {word_count} word essay about {topic}."
prompt = PromptTemplate(
input_variables=["word_count", "topic"], template=formatter_template
)
chain = LLMChain(llm=llm, prompt=prompt)
response = chain.run({"word_count": 100, "topic": "how to make friends"})
return response
except requests.exceptions.RequestException as e:
# Handle any requests-related errors (e.g., network issues, invalid URL)
raise ValueError(f"Error with the API request: {e}")
except json.JSONDecodeError as e:
# Handle any JSON decoding errors (e.g., invalid JSON format)
raise ValueError(f"Error decoding API response as JSON: {e}")
except Exception as e:
# Handle any other errors
raise ValueError(f"Error: {e}")
```
### Expected behavior
According to the documentation I am doing everything correctly, so not sure why its showing a 404 error in a valid url
|
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| 1,873,200,435 |
PR_kwDOIPDwls5ZHW6N
| 9,986 |
TextGen is missing async methods.
|
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closed
| false | null |
[] | null | 1 | 2023-08-30T08:34:04 | 2023-09-03T21:57:40 | 2023-09-03T21:57:40 |
CONTRIBUTOR
| null |
Adding _acall and _astream method that were missing. Preventing streaming during async executions.
@rlancemartin.
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| 1,873,154,024 |
I_kwDOIPDwls5vpg_o
| 9,985 |
ImportError: cannot import name 'HnswVectorSearchAlgorithmConfiguration' from 'azure.search.documents.indexes.models'
|
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open
| false | null |
[] | null | 8 | 2023-08-30T08:07:19 | 2023-12-02T03:48:15 | null |
NONE
| null |
### System Info
I tried to use langchain with Azure Cognitive Search as vector store and got the following Import Error.
langchain version: 0.0.276
azure documents: 11.4.0b8
python version: 3.8
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I tried to use the Azure Cognitive Search as vector store
```
model: str = "text-embedding-ada-002"
search_service = os.environ["AZURE_COGNITIVE_SEARCH_SERVICE_NAME"]
search_api_key = os.environ["AZURE_COGNITIVE_SEARCH_API_KEY"]
vector_store_address: str = f"https://{search_service}.search.windows.net"
vector_store_password: str = search_api_key
# define embedding model for calculating the embeddings
model: str = "text-embedding-ada-002"
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
embedding_function = embeddings.embed_query
# define schema of the json file stored on the index
fields = [
SimpleField(
name="id",
type=SearchFieldDataType.String,
key=True,
filterable=True,
),
SearchableField(
name="content",
type=SearchFieldDataType.String,
searchable=True,
),
SearchField(
name="content_vector",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=len(embedding_function("Text")),
vector_search_configuration="default",
),
SearchableField(
name="metadata",
type=SearchFieldDataType.String,
searchable=True,
),
]
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embedding_function,
fields=fields,
)
```
And I got the following import error
```
Cell In[17], line 72, in azure_search_by_index(question, index_name)
21 # define schema of the json file stored on the index
22 fields = [
23 SimpleField(
24 name="id",
(...)
69 ),
70 ]
---> 72 vector_store: AzureSearch = AzureSearch(
73 azure_search_endpoint=vector_store_address,
74 azure_search_key=vector_store_password,
75 index_name=index_name,
76 embedding_function=embedding_function,
77 fields=fields,
78 )
80 relevant_documentation = vector_store.similarity_search(query=question, k=1, search_type="similarity")
82 context = "\n".join([doc.page_content for doc in relevant_documentation])[:10000]
File /anaconda/envs/jupyter_env/lib/python3.8/site-packages/langchain/vectorstores/azuresearch.py:234, in AzureSearch.__init__(self, azure_search_endpoint, azure_search_key, index_name, embedding_function, search_type, semantic_configuration_name, semantic_query_language, fields, vector_search, semantic_settings, scoring_profiles, default_scoring_profile, **kwargs)
232 if "user_agent" in kwargs and kwargs["user_agent"]:
233 user_agent += " " + kwargs["user_agent"]
--> 234 self.client = _get_search_client(
235 azure_search_endpoint,
236 azure_search_key,
237 index_name,
238 semantic_configuration_name=semantic_configuration_name,
239 fields=fields,
240 vector_search=vector_search,
241 semantic_settings=semantic_settings,
242 scoring_profiles=scoring_profiles,
243 default_scoring_profile=default_scoring_profile,
244 default_fields=default_fields,
245 user_agent=user_agent,
246 )
247 self.search_type = search_type
248 self.semantic_configuration_name = semantic_configuration_name
File /anaconda/envs/jupyter_env/lib/python3.8/site-packages/langchain/vectorstores/azuresearch.py:83, in _get_search_client(endpoint, key, index_name, semantic_configuration_name, fields, vector_search, semantic_settings, scoring_profiles, default_scoring_profile, default_fields, user_agent)
81 from azure.search.documents import SearchClient
82 from azure.search.documents.indexes import SearchIndexClient
---> 83 from azure.search.documents.indexes.models import (
84 HnswVectorSearchAlgorithmConfiguration,
85 PrioritizedFields,
86 SearchIndex,
87 SemanticConfiguration,
88 SemanticField,
89 SemanticSettings,
90 VectorSearch,
91 )
93 default_fields = default_fields or []
94 if key is None:
ImportError: cannot import name 'HnswVectorSearchAlgorithmConfiguration' from 'azure.search.documents.indexes.models' (/anaconda/envs/jupyter_env/lib/python3.8/site-packages/azure/search/documents/indexes/models/__init__.py)
```
### Expected behavior
No import error
|
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I_kwDOIPDwls5vpMKf
| 9,982 |
AttributeError: 'kendra' object has no attribute 'retrieve'
|
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closed
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[] | null | 4 | 2023-08-30T07:14:43 | 2023-12-19T00:48:53 | 2023-12-19T00:48:52 |
NONE
| null |
### System Info
```
import boto3
from langchain.retrievers import AmazonKendraRetriever
retriever = AmazonKendraRetriever(index_id="xxxxxxxxxxxxxxxxxxxx")
retriever.get_relevant_documents("what is the tax")
```
**Facing the below error**
----------------------------------------
```
AttributeError Traceback (most recent call last)
Cell In[48], line 9
5 # retriever = AmazonKendraRetriever(kendraindex='dfba3dce-b6eb-4fec-b98c-abe17a58cf30',
6 # awsregion='us-east-1',
7 # return_source_documents=True)
8 retriever = AmazonKendraRetriever(index_id="7835d77a-470b-4545-9613-508ed8fe82d3")
----> 9 retriever.get_relevant_documents("What is dog")
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/schema/retriever.py:208, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, **kwargs)
206 except Exception as e:
207 run_manager.on_retriever_error(e)
--> 208 raise e
209 else:
210 run_manager.on_retriever_end(
211 result,
212 **kwargs,
213 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/schema/retriever.py:201, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, **kwargs)
199 _kwargs = kwargs if self._expects_other_args else {}
200 if self._new_arg_supported:
--> 201 result = self._get_relevant_documents(
202 query, run_manager=run_manager, **_kwargs
203 )
204 else:
205 result = self._get_relevant_documents(query, **_kwargs)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/retrievers/kendra.py:421, in AmazonKendraRetriever._get_relevant_documents(self, query, run_manager)
407 def _get_relevant_documents(
408 self,
409 query: str,
410 *,
411 run_manager: CallbackManagerForRetrieverRun,
412 ) -> List[Document]:
413 """Run search on Kendra index and get top k documents
414
415 Example:
(...)
419
420 """
--> 421 result_items = self._kendra_query(query)
422 top_k_docs = self._get_top_k_docs(result_items)
423 return top_k_docs
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/langchain/retrievers/kendra.py:390, in AmazonKendraRetriever._kendra_query(self, query)
387 if self.user_context is not None:
388 kendra_kwargs["UserContext"] = self.user_context
--> 390 response = self.client.retrieve(**kendra_kwargs)
391 r_result = RetrieveResult.parse_obj(response)
392 if r_result.ResultItems:
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/botocore/client.py:875, in BaseClient._getattr_(self, item)
872 if event_response is not None:
873 return event_response
--> 875 raise AttributeError(
876 f"'{self._class.name_}' object has no attribute '{item}'"
877 )
```
### 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
Create an kendra index and then use it to run the below code.
import boto3
from langchain.retrievers import AmazonKendraRetriever
retriever = AmazonKendraRetriever(index_id="xxxxxxxxxxxxxxxxxxxx")
retriever.get_relevant_documents("what is the tax")
### Expected behavior
Fetch the document from the index and retrun it like it was happening in previous version.
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| 1,872,965,623 |
I_kwDOIPDwls5voy_3
| 9,981 |
Document Comparison toolkit is not working
|
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open
| false | null |
[] | null | 13 | 2023-08-30T06:02:22 | 2024-01-17T17:59:58 | null |
NONE
| null |
### System Info
First, Thank you so much for your work on Langchain, it's very good.
I am trying to compare two documents following the guide from langchain https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit
I have done exactly the same code:
I have one class to use for the args_schema on the tools creation:
```
class DocumentInput(BaseModel):
question: str = Field()
```
I have created the tools :
```
tools.append(
Tool(
args_schema=DocumentInput,
name=file_name,
description=f"useful when you want to answer questions about {file_name}",
func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
)
)
```
```
agent = initialize_agent(
agent=AgentType.OPENAI_FUNCTIONS,
tools=tools,
llm=llm,
verbose=True,
)
```
And here I am getting the error:
"1 validation error for Tool\nargs_schema\n subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)",
I have changed the args_schema class to :
```
from abc import ABC
from langchain.tools import BaseTool
from pydantic import Field
class DocumentInput(BaseTool, ABC):
question: str = Field()
```
And now the error, I am getting is:
("Value not declarable with JSON Schema, field: name='_callbacks_List[langchain.callbacks.base.BaseCallbackHandler]' type=BaseCallbackHandler required=True",)
I only want to compare the content between two documents, Do you have some example to compare two files which is working? Maybe I am calling wrong the creation of the tools.
### Who can help?
@yajunDai
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have follow exactly the guide for ->https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit to compare two documents, and I am getting the error :
"1 validation error for Tool\nargs_schema\n subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)",
The args_schema class is :
```
class DocumentInput(BaseModel):
question: str = Field()
```
I trying to create the tools :
```
tools.append(
Tool(
args_schema=DocumentInput,
name=file_name,
description=f"useful when you want to answer questions about {file_name}",
func=RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
)
)
```
and here I am getting the error:
"1 validation error for Tool\nargs_schema\n subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)",
After that I am doing this, but I can't get to initialize_agent
```
agent = initialize_agent(
agent=AgentType.OPENAI_FUNCTIONS,
tools=tools,
llm=llm,
verbose=True,
)
```
It's exactly the guide for the Document comparsion on Langchain
### Expected behavior
Expected behaviour, that I can compare the content for two documents without error
|
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| 1,872,945,320 |
I_kwDOIPDwls5vouCo
| 9,980 |
Chroma - retrieve all documents
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[] | null | 1 | 2023-08-30T05:44:19 | 2023-12-06T17:43:05 | 2023-12-06T17:43:04 |
NONE
| null |
### Feature request
When using Chroma vector store, the stored documents can only be retrieved when using a search query. There is no method which allows to load all documents.
### Motivation
The closest thing to retrieving all documents is using
`vectordb._collection.get()` for which the output is a dictionary instead of document. This prevents filtering based on other retrievers which Chroma does not support like TF-IDF and BM25.
### Your contribution
Not yet.
|
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PR_kwDOIPDwls5ZGbQF
| 9,979 |
Diffbot Graph Transformer / Neo4j Graph document ingestion
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[] | null | 2 | 2023-08-30T05:24:06 | 2023-09-06T20:35:45 | 2023-09-06T20:33:00 |
CONTRIBUTOR
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PR_kwDOIPDwls5ZGV6y
| 9,978 |
Add where filter in weaviate similarity search with score
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CONTRIBUTOR
| null |
- Description: Add where filter in weaviate similarity search with score
- Issue: #9853
- Dependencies: -
- Tag maintainer: -
- Twitter handle: -
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mportError: cannot import name 'BaseLanguageModel' from 'langchain.schema' (/usr/local/lib/python3.10/dist-packages/langchain/schema/init.py)
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[] | null | 2 | 2023-08-30T04:54:31 | 2023-12-06T17:43:10 | 2023-12-06T17:43:09 |
NONE
| null |
### System Info
colab notebook
### Who can help?
@hwchase17 @agola
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
getting error: ---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-25-74e62d788fe7> in <cell line: 9>()
7 import openai
8 from langchain.chains import LLMBashChain, LLMChain, RetrievalQA, SimpleSequentialChain
----> 9 from langchain.chains.summarize import load_summarize_chain
10 from langchain.chat_models import ChatOpenAI
11 from langchain.docstore.document import Document
/usr/local/lib/python3.10/dist-packages/langchain/chains/summarize/init.py in <module>
9 from langchain.chains.summarize import map_reduce_prompt, refine_prompts, stuff_prompt
10 from langchain.prompts.base import BasePromptTemplate
---> 11 from langchain.schema import BaseLanguageModel
12
13
ImportError: cannot import name 'BaseLanguageModel' from 'langchain.schema' (/usr/local/lib/python3.10/dist-packages/langchain/schema/init.py)
---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------
```
```
https://colab.research.google.com/drive/1xH7coRd2AnZFdejGQ2nyJWuNrTNMdvSa?usp=sharing
```
### Expected behavior
basic issues running the libraries
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| 1,872,890,614 |
I_kwDOIPDwls5vogr2
| 9,976 |
Generative Agents in LangChain Tool Integration
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[] | null | 14 | 2023-08-30T04:43:59 | 2023-12-14T16:06:18 | 2023-12-14T16:06:17 |
NONE
| null |
### Feature request
The generative Agents demo shows the generic individual conversation.
There are 2 features that I need clarity on. If these exist (if yes how to implement the same) or need to be worked on:
1. Tool integration of the agents. If the agents can integrate to some tool like a calendar or a clock to get the real time actions.
2. The agents wait for the response or the action to be completed by the other agent. Currently the agents reply to the response which is mostly a text response. The requirement here being is that the agents wait for the other agent's action to be completed which is non textual.
### Motivation
The motivation here is the autonomous world simulation that hoes beyond the textual conversation between the agents.
Tool integration can bring in more realistic solutions to come into picture.
### Your contribution
I have read the paper and know the approach.
I can contribute on the development of the feature of that does not exist yet.
|
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GCSFileLoader need to read blob's metadata and populate it to documents metadata
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| false | null |
[] | null | 3 | 2023-08-30T03:58:52 | 2023-10-17T09:53:04 | null |
CONTRIBUTOR
| null |
### Feature request
GCS blobs can have custom metadata defined either in google console or programatic way as shown below
```
from google.cloud import storage
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
metadata = {'from_source': 'https://localhost', 'genre': 'sci-fi'}
blob.metadata = metadata
blob.upload_from_filename("/home/jupyter/svc-mlp-staging.json", if_generation_match=0)
```
GCSFileLoader can get blob's metadata if present and populate the same to document's metadata before returning docs.
### Motivation
1. This feature can help load the documents with the required custom metadata.
2. Splits (based on splitter) and vector embeddings of splits will be identified by custom metadata in vector store
### Your contribution
Interested in taking this up
|
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| 1,872,816,079 |
PR_kwDOIPDwls5ZGDex
| 9,974 |
rm mutable defaults
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closed
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[] | null | 1 | 2023-08-30T03:20:12 | 2023-08-30T03:36:28 | 2023-08-30T03:36:27 |
COLLABORATOR
| null | null |
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| 1,872,811,739 |
I_kwDOIPDwls5voNbb
| 9,973 |
azure.search.documents.indexes.models._edm no attribute "Single" under Langchain.AzureSearch()
|
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[] | null | 4 | 2023-08-30T03:14:22 | 2023-11-07T08:04:12 | null |
NONE
| null |
### System Info
Platform: MacOS 13.5
Python Version: 3.10.11
Langchain: 0.0.257
Azure Search: 1.0.0b2
Azure Search Documents: 11.4.0b6
Openai: 0.27.8
Issue: AttributeError: module 'azure.search.documents.indexes.models._edm' has no attribute 'Single'
Code: `az_search = AzureSearch(azure_search_endpoint=os.getenv('AZURE_COGNITIVE_SEARCH_SERVICE_NAME'),
azure_search_key=os.getenv('AZURE_COGNITIVE_SEARCH_API_KEY'),
index_name=os.getenv('AZURE_COGNITIVE_SEARCH_INDEX_NAME'),
embedding_function=embeddings.embed_query)`
I read from another post here: [https://github.com/langchain-ai/langchain/issues/8917](url) that this is caused by a version miss match. So I downgraded my Azure search-documents package version to 11.4.0b6. But the same error occurred.
I've also tried using langchin==0.0.245 or langchain==0.0.247 but didn't solve the issue.
### 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
```
os.environ["AZURE_COGNITIVE_SEARCH_INDEX_NAME"]="vectordb001index"
embeddings = OpenAIEmbeddings(deployment_id="vectorDBembedding", chunk_size=1)
az_search = AzureSearch(azure_search_endpoint=os.getenv('AZURE_COGNITIVE_SEARCH_SERVICE_NAME'),
azure_search_key=os.getenv('AZURE_COGNITIVE_SEARCH_API_KEY'),
index_name=os.getenv('AZURE_COGNITIVE_SEARCH_INDEX_NAME'),
embedding_function=embeddings.embed_query)
```
**Error Output**
```
File [~/Desktop/.../azuresearch.py:221](https://file+.vscode-resource.vscode-cdn.net/.../azuresearch.py:221), in AzureSearch.__init__(self, azure_search_endpoint, azure_search_key, index_name, embedding_function, search_type, semantic_configuration_name, semantic_query_language, fields, vector_search, semantic_settings, scoring_profiles, default_scoring_profile, **kwargs)
206 # Initialize base class
207 self.embedding_function = embedding_function
208 default_fields = [
209 SimpleField(
210 name=FIELDS_ID,
211 type=SearchFieldDataType.String,
212 key=True,
213 filterable=True,
214 ),
215 SearchableField(
216 name=FIELDS_CONTENT,
217 type=SearchFieldDataType.String,
218 ),
...
230 ]
231 user_agent = "langchain"
232 if "user_agent" in kwargs and kwargs["user_agent"]:
AttributeError: module 'azure.search.documents.indexes.models._edm' has no attribute 'Single'
```
### Expected behavior
Program running with no error interruption
|
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| 1,872,777,987 |
I_kwDOIPDwls5voFMD
| 9,972 |
create_structured_output_chain does not work with with AzureChatOpenAI model?
|
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| false | null |
[] | null | 2 | 2023-08-30T02:28:18 | 2023-11-01T10:18:32 | null |
NONE
| null |
### System Info
Python 3.11.4
langchain-0.0.276
currently troubleshooting on a Windows 11 workstation in a notebook in VSCode.
### Who can help?
@hwchase17 @ag
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm trying to use a structured output chain with some defined Pydantic classes. The chain works when using ChatOpenAI model but not AzureChatOpenAI. I'm able to generate non-chained chat completions with AzureChatOpenAI, so I'm pretty confident the issue isn't with my configuration of the AzureChatOpenAI model.
Representative code:
```
# ChatOpenAI using native OpenAI endpoint
openai_model = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
# AzureChatOpenAI using private endpoint through Azure OpenAI Service
azure_model = AzureChatOpenAI(
openai_api_base="[hiding my api base]",
openai_api_version="2023-07-01-preview",
deployment_name="[hiding my model deployment name]",
model_name = "[hiding my model name]",
openai_api_key="[hiding my API key]",
openai_api_type="azure",
temperature = 1
)
# define representative singleton class
class GeneratedItem(BaseModel):
"""Information about a generated item."""
item_nickname: str = Field(..., description="A creative nickname for an item that might be found in some room")
item_purpose: str = Field(..., description="The purpose of the item")
# define the plural class as a Sequence of the above singletons
class GeneratedItems(BaseModel):
"""information about all items generated"""
items: Sequence[GeneratedItem] = Field(..., description="A sequence of items")
# define messages for a prompt
prompt_msgs = [
SystemMessage(
content="You are a world class algorithm for generating information in structured formats."
),
HumanMessage(
content="Use the given format to generate 2 items that might be in a room described as follows"
),
HumanMessagePromptTemplate.from_template("{input}"),
HumanMessage(content="Tips: Make sure to answer in the correct format"),
]
# define the prompt using ChatPromptTemplate
prompt = ChatPromptTemplate(messages=prompt_msgs)
# define and execute structured output chain with ChatOpenAI model
chain1 = create_structured_output_chain(GeneratedItems, openai_model, prompt, verbose=False)
chain1.run("A living room with green chairs and a wooden coffee table")
# define and execute structued output chain with AzureChatOpenAI model
chain2 = create_structured_output_chain(GeneratedItems, azure_model, prompt, verbose=False)
chain2.run("A living room with green chairs and a wooden coffee table")
```
### Expected behavior
In the above code, the `chain1.run()` using ChatOpenAI executes successfully returning something like the below:
{'items': [{'item_nickname': 'green chairs', 'item_purpose': 'seating'},
{'item_nickname': 'wooden coffee table', 'item_purpose': 'surface'}]}
However, when executing `chain2.run()` which uses AzureChatOpenAI, this behavior is not replicated. Instead, I receive the below errors (including full traceback to help troubleshoot)
```
KeyError Traceback (most recent call last)
Cell In[23], line 55
50 # chain1 = create_structured_output_chain(GeneratedItems, openai_model, prompt, verbose=False)
51 # chain1.run("A living room with green chairs and a wooden coffee table")
54 chain2 = create_structured_output_chain(GeneratedItems, azure_model, prompt, verbose=False)
---> 55 chain2.run("A living room with green chairs and a wooden coffee table")
File ...\.venv\Lib\site-packages\langchain\chains\base.py:441, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
439 if len(args) != 1:
440 raise ValueError("`run` supports only one positional argument.")
--> 441 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
442 _output_key
443 ]
445 if kwargs and not args:
446 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
447 _output_key
448 ]
File ...\.venv\Lib\site-packages\langchain\chains\base.py:244, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, include_run_info)
242 except (KeyboardInterrupt, Exception) as e:
243 run_manager.on_chain_error(e)
--> 244 raise e
245 run_manager.on_chain_end(outputs)
246 final_outputs: Dict[str, Any] = self.prep_outputs(
...
--> 103 content = _dict["content"] or "" # OpenAI returns None for tool invocations
104 if _dict.get("function_call"):
105 additional_kwargs = {"function_call": dict(_dict["function_call"])}
KeyError: 'content'
```
Ideally this would return structured output for AzureChatOpenAI model in exactly the same manner as it does when using a ChatOpenAI model. Maybe I missed something in the docs, but thinking this is a source-side issue with AzureChatOpenAI not containing/creating the `content` key in the `_dict` dictionary.
Please let me know if there are any workarounds or solutions I should attempt and/or some documentation I may not have found.
|
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| 1,872,777,195 |
PR_kwDOIPDwls5ZF7W0
| 9,971 |
Check memory address
|
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[] | null | 1 | 2023-08-30T02:27:08 | 2023-08-30T22:30:24 | 2023-08-30T22:30:23 |
COLLABORATOR
| null |
Don't want to dup the collector but can have multiple
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| 1,872,776,933 |
PR_kwDOIPDwls5ZF7TU
| 9,970 |
Update error message
|
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[] | null | 1 | 2023-08-30T02:26:46 | 2023-08-31T00:42:56 | 2023-08-31T00:42:55 |
COLLABORATOR
| null |
in evals
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| 1,872,762,294 |
PR_kwDOIPDwls5ZF4Nd
| 9,969 |
update_forward_refs for Run
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[] | null | 2 | 2023-08-30T02:06:22 | 2023-08-31T22:25:59 | 2023-08-31T22:25:59 |
CONTRIBUTOR
| null |
Adds a call to Pydantic's `update_forward_refs` for the `Run` class (in addition to the `ChainRun` and `ToolRun` classes, for which that method is already called). Without it, the self-reference of child classes (type `List[Run]`) is problematic. For example:
```python
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from wandb.integration.langchain import WandbTracer
llm = OpenAI()
prompt = PromptTemplate.from_template("1 + {number} = ")
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[StdOutCallbackHandler(), WandbTracer()])
print(chain.run(number=2))
```
results in the following output before the change
```
WARNING:root:Error in on_chain_start callback: field "child_runs" not yet prepared so type is still a ForwardRef, you might need to call Run.update_forward_refs().
> Entering new LLMChain chain...
Prompt after formatting:
1 + 2 =
WARNING:root:Error in on_chain_end callback: No chain Run found to be traced
> Finished chain.
3
```
but afterwards the callback error messages are gone.
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| 1,872,754,726 |
PR_kwDOIPDwls5ZF2oZ
| 9,968 |
fixed openai_functions api_response format args err
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[] | null | 2 | 2023-08-30T01:55:50 | 2023-08-31T07:49:24 | 2023-08-31T07:49:20 |
CONTRIBUTOR
| null |
root cause: args may not have a key (params) resulting in an error
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZFt5z
| 9,967 |
Bagatur/vearch
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[] | null | 1 | 2023-08-30T00:59:51 | 2023-08-30T01:12:04 | 2023-08-30T01:02:32 |
COLLABORATOR
| null |
CI not running on original pr
|
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| 1,872,654,744 |
PR_kwDOIPDwls5ZFhTu
| 9,966 |
Bagatur/add data anonymizer
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[] | null | 1 | 2023-08-29T23:52:02 | 2023-08-31T07:49:37 | 2023-08-31T07:49:37 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZFVcA
| 9,965 |
docs: `integrations/tools` consistency
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[] | null | 1 | 2023-08-29T22:41:48 | 2023-08-30T01:35:15 | 2023-08-30T01:04:02 |
COLLABORATOR
| null |
Updated titles, descriptions into consistent format.
|
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PR_kwDOIPDwls5ZFVEz
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Feat/add bedrock claude chat (resolve conflicts with master)
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[] | null | 1 | 2023-08-29T22:39:30 | 2023-08-29T22:44:03 | 2023-08-29T22:43:44 |
NONE
| null |
This is a follow-up PR that resolves merge conflicts with the master for #8017
|
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| 1,872,579,339 |
PR_kwDOIPDwls5ZFRhq
| 9,963 |
don't emit none from eval config
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[] | null | 1 | 2023-08-29T22:20:09 | 2023-08-29T23:14:34 | 2023-08-29T23:14:33 |
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PR_kwDOIPDwls5ZFJ2r
| 9,961 |
Bagatur/activeloopai runtime arg
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PR_kwDOIPDwls5ZFCh3
| 9,960 |
docs: `llms/google vertex AI` example update
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[] | null | 1 | 2023-08-29T21:33:08 | 2023-08-29T22:24:10 | 2023-08-29T22:07:18 |
COLLABORATOR
| null |
Updated title, description, added sections.
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I_kwDOIPDwls5vnB5I
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SQL Chain Result - Error
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[] | null | 4 | 2023-08-29T21:27:00 | 2023-08-30T19:39:38 | 2023-08-30T19:32:09 |
NONE
| null |
### System Info
**### I´m directly providing these arguments to the LLMs via the prompt:**
**SOURCE CODE:**
from langchain import OpenAI, SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
from langchain.chains import create_sql_query_chain
from langchain.prompts.prompt import PromptTemplate
import environ
env = environ.Env()
environ.Env.read_env()
API_KEY = env('OPENAI_API_KEY')
db = SQLDatabase.from_uri(
f"postgresql+psycopg2://postgres:{env('DBPASS')}@localhost:5432/{env('DATABASE')}",
)
llm = OpenAI(temperature=0, openai_api_key=API_KEY)
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Use the following format:
Question:"Question here"
SQLQuery:"SQL Query to run"
SQLResult:"Result of the SQLQuery"
Answer:"Final answer here"
Only use the following tables:
{table_info}
If someone asks for the table foobar, they really mean the tasks table.
Question: {input}"""
PROMPT = PromptTemplate(
input_variables=["input", "table_info", "dialect"], template=_DEFAULT_TEMPLATE
)
db_chain = SQLDatabaseChain.from_llm(llm, db, prompt=PROMPT, verbose=True, use_query_checker=True)
def get_prompt():
print("Digite 'exit' para sair")
while True:
prompt = input("Entre c/ uma pergunta (prompt): ")
if prompt.lower() == 'exit':
print('Saindo...')
break
else:
try:
result = db_chain(prompt)
print(result)
except Exception as e:
print(e)
get_prompt()
**BUT THE RESULT IS WITH [ ] AND NUMBERS:**
←[1m> Entering new SQLDatabaseChain chain...←[0m
how many tasks do we have?
SQLQuery:←[32;1m←[1;3mSELECT COUNT(*) FROM tasks;←[0m
SQLResult: ←[33;1m←[1;3m[(6,)]←[0m
Answer:←[32;1m←[1;3mWe have 6 tasks.←[0m
←[1m> Finished chain.←[0m
**HOW CAN I FIX IT?**
### 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
I need the answes without these numbers: [32;1m←[1;3m
### Expected behavior
I need the answes without these numbers: [32;1m←[1;3m
|
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| 1,872,491,296 |
PR_kwDOIPDwls5ZE90t
| 9,958 |
docs: `youtube_search` tool example update
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[] | null | 1 | 2023-08-29T21:20:57 | 2023-09-01T20:45:26 | 2023-09-01T20:32:28 |
COLLABORATOR
| null |
Added a link to source package; updated title, description.
|
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| 1,872,458,532 |
PR_kwDOIPDwls5ZE2Wx
| 9,957 |
fix #3117
|
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[] | null | 1 | 2023-08-29T21:02:54 | 2023-08-31T14:29:51 | 2023-08-31T14:29:50 |
COLLABORATOR
| null |
fix #3117
|
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| 1,872,444,158 |
I_kwDOIPDwls5vmzr-
| 9,956 |
Multi Tenant Support for Weaviate
|
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[] | null | 4 | 2023-08-29T20:56:22 | 2023-11-24T22:39:28 | null |
CONTRIBUTOR
| null |
### Feature request
Weaviate introduced multi-tenancy support in version 1.20
https://weaviate.io/blog/multi-tenancy-vector-search
### Motivation
This can help users using Langchain + Weaviate at scale, ingesting documents and attaching tenants to it.
### Your contribution
I have implemented, but would need some help to check if everything is ok and in accordance with LangChain.
Also, I would like help on the as_retriver, as I was not able to implement multitenant on it, Yet.
the code is living here: https://github.com/dudanogueira/langchain/tree/weaviate-multitenant
|
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PR_kwDOIPDwls5ZEo4H
| 9,955 |
Update __init__.py
|
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[] | null | 7 | 2023-08-29T20:33:14 | 2023-09-05T21:39:08 | 2023-09-03T22:02:59 |
CONTRIBUTOR
| null |
Add SQLDatabaseSequentialChain Class to __init__.py so it can be accessed and used
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- Description: SQLDatabaseSequentialChain is not found when importing Langchain_experimental package, when I open __init__.py Langchain_expermental.sql, I found that SQLDatabaseSequentialChain is imported and add to __all__ list
- Issue: SQLDatabaseSequentialChain is not found in Langchain_experimental package
- Dependencies: None,
- Tag maintainer: None,
- Twitter handle: None,
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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PR_kwDOIPDwls5ZEoZf
| 9,954 |
Weakref to tracer
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[] | null | 1 | 2023-08-29T20:32:14 | 2023-08-30T02:27:24 | 2023-08-30T02:27:23 |
COLLABORATOR
| null |
Prevent memory/thread leakage
|
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PR_kwDOIPDwls5ZEfUf
| 9,953 |
mv base cache to schema
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[] | null | 1 | 2023-08-29T20:11:10 | 2023-08-30T15:10:53 | 2023-08-30T15:10:52 |
COLLABORATOR
| null |
if you remove all other imports from langchain.init it exposes this circular dep
```console
File "/Users/bagatur/langchain/libs/langchain/langchain/__init__.py", line 6, in <module>
from langchain.cache import BaseCache
File "/Users/bagatur/langchain/libs/langchain/langchain/cache.py", line 56, in <module>
from langchain.embeddings.base import Embeddings
File "/Users/bagatur/langchain/libs/langchain/langchain/embeddings/__init__.py", line 51, in <module>
from langchain.embeddings.sagemaker_endpoint import SagemakerEndpointEmbeddings
File "/Users/bagatur/langchain/libs/langchain/langchain/embeddings/sagemaker_endpoint.py", line 4, in <module>
from langchain.llms.sagemaker_endpoint import ContentHandlerBase
File "/Users/bagatur/langchain/libs/langchain/langchain/llms/__init__.py", line 22, in <module>
from langchain.llms.ai21 import AI21
File "/Users/bagatur/langchain/libs/langchain/langchain/llms/ai21.py", line 6, in <module>
from langchain.llms.base import LLM
File "/Users/bagatur/langchain/libs/langchain/langchain/llms/base.py", line 50, in <module>
from langchain.prompts.base import StringPromptValue
File "/Users/bagatur/langchain/libs/langchain/langchain/prompts/__init__.py", line 40, in <module>
from langchain.prompts.example_selector import (
File "/Users/bagatur/langchain/libs/langchain/langchain/prompts/example_selector/__init__.py", line 4, in <module>
from langchain.prompts.example_selector.semantic_similarity import (
File "/Users/bagatur/langchain/libs/langchain/langchain/prompts/example_selector/semantic_similarity.py", line 9, in <module>
from langchain.vectorstores.base import VectorStore
File "/Users/bagatur/langchain/libs/langchain/langchain/vectorstores/__init__.py", line 50, in <module>
from langchain.vectorstores.matching_engine import MatchingEngine
File "/Users/bagatur/langchain/libs/langchain/langchain/vectorstores/matching_engine.py", line 10, in <module>
from langchain.embeddings import TensorflowHubEmbeddings
ImportError: cannot import name 'TensorflowHubEmbeddings' from partially initialized module 'langchain.embeddings' (most likely due to a circular import) (/Users/bagatur/langchain/libs/langchain/langchain/embeddings/__init__.py)
```
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I_kwDOIPDwls5vmaPf
| 9,952 |
ConversationalRetrievalChain [got multiple argument for question_generator]
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NONE
| null |
### System Info
Getting error: got multiple values for keyword argument- question_generator .
return cls(\nTypeError: langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain() got multiple values for keyword argument \'question_generator\'', 'SystemError'
`Qtemplate = (
"Combine the chat history and follow up question into "
"a standalone question. Chat History: {chat_history}"
"Follow up question: {question} withoud changing the real meaning of the question itself."
)
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(Qtemplate)
question_generator_chain = LLMChain(llm=OpenAI(openai_api_key=openai.api_key), prompt=CONDENSE_QUESTION_PROMPT)
chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=self.vector_store.as_retriever(),
combine_docs_chain_kwargs=chain_type_kwargs,
verbose=True,
return_source_documents=True,
get_chat_history=lambda h: h,
memory=window_memory,
question_generator=question_generator_chain
)`
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduct the behaviour:
1. Generate standalone question, which does not change the meaning of question, if it is changing meaning of question, keep question as it is.
2. Generate output using memory and get most accurate answer.
### Expected behavior
Expecting the right code for implementing same functionality.
|
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PR_kwDOIPDwls5ZEYoK
| 9,951 |
docs: Azure AD Authentication for Azure OpenAI
|
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[] | null | 2 | 2023-08-29T19:47:48 | 2023-08-29T21:29:28 | 2023-08-29T21:29:28 |
CONTRIBUTOR
| null |
# Description
This PR adds additional documentation on how to use Azure Active Directory to authenticate to an OpenAI service within Azure. This method of authentication allows organizations with more complex security requirements to use Azure OpenAI.
# Issue
N/A
# Dependencies
N/A
# Twitter
https://twitter.com/CamAHutchison
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PR_kwDOIPDwls5ZEHGK
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fix intro docs
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[] | null | 1 | 2023-08-29T18:49:03 | 2023-08-29T19:29:23 | 2023-08-29T18:50:08 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5ZEGWQ
| 9,949 |
docs: `memory types` menu
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[] | null | 1 | 2023-08-29T18:46:20 | 2023-08-29T22:23:46 | 2023-08-29T22:05:23 |
COLLABORATOR
| null |
The [Memory Types](https://python.langchain.com/docs/modules/memory/types/) menu is clogged with unnecessary wording.
I've made it more concise by simplifying titles of the example notebooks.
As results, menu is shorter and better for comprehend.
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PR_kwDOIPDwls5ZD8YJ
| 9,948 |
feat: PromptGuard takes a list of str
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[] | null | 2 | 2023-08-29T18:10:37 | 2023-08-29T19:22:30 | 2023-08-29T19:22:30 |
CONTRIBUTOR
| null |
Recently we made the decision that PromptGuard takes a list of strings instead of a string.
@ggroode implemented the integration change.
cc @baskaryan
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PR_kwDOIPDwls5ZDuty
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docs: `memory` menu
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[] | null | 1 | 2023-08-29T17:24:01 | 2023-08-29T22:23:59 | 2023-08-29T22:06:11 |
COLLABORATOR
| null |
The [Memory](https://python.langchain.com/docs/modules/memory/) menu is clogged with unnecessary wording.
I've made it more concise by simplifying titles of the example notebooks.
As results, menu is shorter and better for comprehend.
|
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PR_kwDOIPDwls5ZDi3y
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Sort by most recent chatIDs
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COLLABORATOR
| null |
When we `lazy_load` iMessage chats, return chats w/ most recent msg first (matches what is visualized in app).
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| 1,872,039,977 |
PR_kwDOIPDwls5ZDhqo
| 9,945 |
Fix import
|
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[] | null | 1 | 2023-08-29T16:42:38 | 2023-08-29T19:38:43 | 2023-08-29T19:38:42 |
COLLABORATOR
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| 1,872,033,912 |
PR_kwDOIPDwls5ZDgWC
| 9,944 |
Fix json parsing error of MULTI_PROMPT_ROUTER_TEMPLATE
|
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[] | null | 2 | 2023-08-29T16:38:56 | 2023-09-03T22:00:50 | 2023-09-03T22:00:50 |
CONTRIBUTOR
| null |
The output at times lacks the closing markdown code block. The prompt is changed to explicitly request the closing backticks.
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Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,872,022,615 |
PR_kwDOIPDwls5ZDd6I
| 9,943 |
Speed up openai tests
|
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[] | null | 1 | 2023-08-29T16:30:30 | 2023-08-29T21:30:42 | 2023-08-29T21:30:41 |
COLLABORATOR
| null |
Saves ~8-10 seconds from total unit tests times
|
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| 1,872,021,415 |
I_kwDOIPDwls5vlMen
| 9,942 |
Optional Arguments Treated as Required by "format_tool_to_openai_function"
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[] | null | 3 | 2023-08-29T16:29:41 | 2023-12-06T17:43:15 | 2023-12-06T17:43:14 |
NONE
| null |
### System Info
I have a tool with one required argument _chart_data_ and one optional argument _chart_title_. The tool is defined using the BaseModel class from Pydantic and is decorated with @tool("charts", args_schema=ChartInput).
However, optional arguments are pushed into the 'required' list that is being passed to OpenAI.
Do you have any suggestions for resolving this issue? GPT-3.5 consistently prompts for the chart_title argument, even though it's supposed to be optional.
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Here's the code snippet:
langchain==0.0.274
```
class ChartInput(BaseModel):
chart_data: list = Field(..., description="Data for chart")
chart_title: Optional[str] = Field(None, description="The title for the chart.")
@tool("charts", args_schema=ChartInput)
def charts_tool(chart_data: list, chart_title: Optional[str]=None):
'''useful when creating charts'''
return 'chart image url'
```
### Expected behavior
When printing out the output of: format_tool_to_openai_function(charts_tool) , I see can chart_title which is an optional argument is pushed into the required args list.
`{'name': 'charts', 'description': 'charts(chart_data: list, chart_title: Optional[str] = None) - useful when creating charts', 'parameters': {'type': 'object', 'properties': {'chart_data': {'title': 'Chart Data', 'description': 'data for chart', 'type': 'array', 'items': {}}, 'chart_title': {'title': 'Chart Title', 'description': 'The title for the chart.', 'type': 'string'}}, 'required': ['chart_data', 'chart_title']}}`
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| 1,872,009,278 |
PR_kwDOIPDwls5ZDbEr
| 9,941 |
Add neo4j provider page
|
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[] | null | 2 | 2023-08-29T16:21:21 | 2023-08-29T17:09:52 | 2023-08-29T17:09:52 |
CONTRIBUTOR
| null | null |
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| 1,872,004,545 |
PR_kwDOIPDwls5ZDaE5
| 9,940 |
perf: update repositories
|
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NONE
| null |
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Replace this entire comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
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. These live is docs/extras directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17, @rlancemartin.
-->
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| 1,871,995,976 |
PR_kwDOIPDwls5ZDYRT
| 9,939 |
addition of cosine distance function for faiss
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[] | null | 3 | 2023-08-29T16:12:29 | 2023-09-01T09:57:21 | 2023-08-29T17:29:52 |
CONTRIBUTOR
| null |
- Description: added the _cosine_relevance_score_fn to _select_relevance_score_fn of faiss.py to enable the use of cosine distance for similarity for this vector store and to comply with the Error Message, that implies, that cosine should be a valid distance strategy
- Issue: no relevant Issue found, but needed this function myself and tested it in a private repo
- Dependencies: none
|
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| 1,871,984,334 |
PR_kwDOIPDwls5ZDV1F
| 9,938 |
Add security notices on PAL and CPAL experimental chains.
|
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[] | null | 1 | 2023-08-29T16:04:54 | 2023-08-29T17:51:58 | 2023-08-29T17:51:57 |
COLLABORATOR
| null |
Clearly document that the PAL and CPAL techniques involve generating code, and that such code must be properly sandboxed and given appropriate narrowly-scoped credentials in order to ensure security.
While our implementations include some mitigations, Python and SQL sandboxing is well-known to be a very hard problem and our mitigations are no replacement for proper sandboxing and permissions management. The implementation of such techniques must be performed outside the scope of the Python process where this package's code runs, so its correct setup and administration must therefore be the responsibility of the user of this code.
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PR_kwDOIPDwls5ZDQBo
| 9,937 |
lint for pydantic imports
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[] | null | 2 | 2023-08-29T15:48:31 | 2023-08-31T19:55:30 | 2023-08-31T19:55:29 |
COLLABORATOR
| null |
Catch pydantic imports
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PR_kwDOIPDwls5ZDJPU
| 9,936 |
Add notice about security-sensitive experimental code to experimental README.
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[] | null | 1 | 2023-08-29T15:30:30 | 2023-08-29T18:21:31 | 2023-08-29T18:21:30 |
COLLABORATOR
| null |
It renders like this: https://github.com/langchain-ai/langchain/tree/pg/experimental-readme/libs/experimental
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PR_kwDOIPDwls5ZDDko
| 9,933 |
Add indexing support for postgresql
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[] | null | 1 | 2023-08-29T15:16:07 | 2023-08-31T14:27:10 | 2023-08-31T14:27:09 |
COLLABORATOR
| null |
Add support to postgresql for the SQL Manager Record
This code was tested locally. I'm looking at how to add testing with postgres in a separate PR.
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I_kwDOIPDwls5vknJl
| 9,932 |
Functionality for prompts based on decision tree / flow charts.
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[] | null | 10 | 2023-08-29T15:02:20 | 2024-01-10T12:27:24 | 2023-12-21T16:06:54 |
NONE
| null |
### Feature request
I want to request functionality for decision tree / flow chart like prompt architecture. The idea is that there would be a Prompt Tree that starts on a specific branch then allows the LLM to select new branches as part of its toolkit. Each branch would have its own prompts meaning that the AI does not need to be given all the information up front and instead can break down its commands into bite sized chunks that it sees at each branch in the tree.
### Motivation
This would help chatbot workflows by limiting the amount of information the LLM sees at each point in time, it can collect variables through different branches of the tree that it will use later, and it would improve reliability with LLM outputs because it would be easier to implement checks. It also could eliminate the need for a scratchpad, which can become costly if abused by the LLM.
Also, this is a feature that is available in other systems such as [LLMFlows](https://github.com/stoyan-stoyanov/llmflows) and [Amazon Lex](https://aws.amazon.com/lex/). And from what I have seen it is frequently on message boards here.
### Your contribution
I have made a simple example script to show how this could work in principle. However, I do not have experience contributing to open source projects so I am not sure what formatting mistakes I may be making, nor where exactly in the object heirarchy this should belong (is this a type of Prompt? Or Agent?). I would love to learn about what is needed to incorporate this into the LangChain functionality.
In my example I make a PromptTree class which stores the state and can access the current prompt. Inside the tree are a variety of branches which point to eachother according to a dictionary. Each branch produces a tool which allows the AI to switch branches by updating the prompttree.
```python
# Import libraries
import ast
from pydantic.v1 import BaseModel, Field
from langchain.tools import Tool
from langchain.schema import HumanMessage, AIMessage, SystemMessage, FunctionMessage
from langchain.tools import format_tool_to_openai_function
from langchain.chat_models import ChatOpenAI
### Define PromptBranch ###
# Declare function name variable
SELECT_BRANCH = 'select_branch'
UPDATE_INSIGHT = 'update_insight'
# Create PromptTreeBranch class
class PromptBranch:
"""A branch in the PromptTree."""
# Declare PromptBranch variables
description = None # Default description of the branch
header = None # Header prompt
footer = None # Footer prompt
children = {} # Dictionary of children branches with descriptions. Format={name: description (None for default)}
initial_state = {} # Initial state of the branch
pass_info = {} # Additional info to be passed to children
insights = {} # Dictionary of insights that the AI can update. Format={name: description}
# Get branch ID
@property
def branch_id(self):
"""Get the branch ID."""
return type(self).__name__
def __init__(self, parent, **kwargs):
"""Initialize the PromptBranch."""
self.parent = parent
self.initialize_state(**kwargs)
return
def initialize_state(self, **kwargs):
"""Initialize the branch state."""
# We allow kwargs to be passed in case the branch needs to be initialized with additional info
self.state = {
**self.initial_state,
'insights': {x: None for x in self.insights.keys()} # Initialize insights to None
}
return
def __call__(self, messages):
"""Call the PromptBranch."""
return (
self.get_prompt(messages),
self.get_tools(),
)
def get_pass_info(self):
"""Pass info to children."""
return self.pass_info
def get_prompt(self, messages):
"""Get the prompt."""
# Initialze prompt
prompt = []
# Add preamble
preamble = self.parent.preamble
if preamble is not None:
prompt.append(SystemMessage(content=preamble))
# Add header
header = self.get_header()
if header is not None:
prompt.append(SystemMessage(content=header))
# Add messages
prompt += messages
# Add footer
footer = self.get_footer()
if footer is not None:
prompt.append(SystemMessage(content=footer))
# Add insights
insights = self.get_insights()
if insights is not None:
prompt.append(SystemMessage(content=insights))
# Return
return prompt
def get_header(self):
"""Get header."""
return self.header
def get_footer(self):
"""Get footer."""
return self.footer
def get_insights(self):
"""Get insights."""
if len(self.insights) == 0:
return None
else:
insights = f"Your insights so far are:"
for name, state in self.state['insights'].items():
insights += f"\n{name}: {state}"
return insights
def get_tools(self):
"""Get tools."""
# Initialize tools
tools = []
# Add switch branch tool
if len(self.children) > 0:
tools.append(self._tool_switch_branch())
# Add update insights tool
if len(self.insights) > 0:
tools.append(self._tool_update_insight())
# Return
return tools
def _tool_switch_branch(self):
"""Create tool to select next branch."""
# Get variables
tool_name = SELECT_BRANCH
children = self.children
# Create tool function
tool_func = self.switch_branch
# Create tool description
tool_description = "Select the next branch to continue the conversation. Your options are:"
for branch_id, branch_description in children.items():
if branch_description is None:
branch_description = self.parent.all_branches[branch_id].description
tool_description += f"\n{branch_id}: {branch_description}"
# Create tool schema
class ToolSchema(BaseModel):
branch: str = Field(
description="Select next branch.",
enum=list(children.keys()),
)
# Create tool
tool_obj = Tool(
name=tool_name,
func=tool_func,
description=tool_description,
args_schema=ToolSchema,
)
# Return
return tool_obj
def _tool_update_insight(self):
"""Create tool to update an insight."""
# Get variables
tool_name = UPDATE_INSIGHT
insights = self.insights
# Create tool function
tool_func = self.update_insight
# Create tool description
tool_description = "Update an insight. You can choose to update any of the following insights:"
for name, state in insights.items():
tool_description += f"\n{name}: {state}"
# Create tool schema
class ToolSchema(BaseModel):
insight: str = Field(
description="Select insight to update.",
enum=list(insights.keys()),
)
value: str = Field(
description="New value of the insight.",
)
# Create tool
tool_obj = Tool(
name=tool_name,
func=tool_func,
description=tool_description,
args_schema=ToolSchema,
)
# Return
return tool_obj
def switch_branch(self, branch):
"""Switch to a new branch."""
# Switch parent tree branch
self.parent.branch = self.parent.all_branches[branch](parent=self.parent, **self.get_pass_info())
# Return function message
message = FunctionMessage(
name=SELECT_BRANCH,
content=f"You have switched to the {branch} branch.",
additional_kwargs={'internal_function': True},
)
return message
def update_insight(self, insight, value):
"""Update an insight."""
# Update insight
self.state['insights'][insight] = value
# Return function message
message = FunctionMessage(
name=UPDATE_INSIGHT,
content=f"You have updated the {insight} insight to {value}.",
additional_kwargs={'internal_function': True},
)
return message
### Define PromptTree ###
# Create PromptTree class
class PromptTree:
"""A decision tree for prompting the AI."""
# Declare PromptTree variables
preamble = None # System prompt to put before each branch prompt
first_branch = None # Name of first branch to start the prompt tree
all_branches = {} # Dictionary of all branches in the tree. Format={branch_id: branch_class}
def __init__(self):
"""Initialize the PromptTree branch state."""
self.branch = self.all_branches[self.first_branch](parent=self)
return
def __call__(self, messages, **kwargs):
"""Call the PromptTree."""
return self.branch(messages, **kwargs)
def get_state(self):
"""Get the current branch state."""
return {
'branch_id': self.branch.branch_id,
'branch_state': self.branch.state,
}
def load_state(self, state):
"""Load a branch from the state."""
branch_id = state['branch_id']
branch_state = state['branch_state']
if branch_id not in self.all_branches:
raise ValueError(f"Unknown branch_id: {branch_id}")
self.branch = self.all_branches[branch_id](parent=self)
self.branch.state = branch_state
return
### Define TreeAgent ###
# Create TreeAgent class
class TreeAgent:
"""An AI agent based on the PromptTree class."""
def __init__(self, tree, model):
"""Initialize the TreeAgent."""
self.tree = tree
self.model = model
return
def __call__(self, messages, **kwargs):
"""Call the TreeAgent."""
return self.respond(messages, **kwargs)
def get_state(self):
"""Get the current state of the TreeAgent."""
return self.tree.get_state()
def load_state(self, state):
"""Load the state of the TreeAgent."""
self.tree.load_state(state)
return
def respond(self, messages):
"""Respond to the messages."""
# Initialize new messages
new_messages = []
# Loop until no function calls
while True:
# Get the prompt
prompt, tools = self.tree(messages+new_messages)
# Get the response
funcs = [format_tool_to_openai_function(t) for t in tools]
response = self.model.predict_messages(prompt, functions=funcs)
new_messages.append(response)
# Check for function calls
if 'function_call' in new_messages[-1].additional_kwargs:
# Get function call
func_call = new_messages[-1].additional_kwargs['function_call']
func_name = func_call['name']
func_args = ast.literal_eval(func_call['arguments'])
func = [x.func for x in tools if x.name == func_name][0]
# Call the function
func_response = func(**func_args)
new_messages.append(func_response)
continue
else:
# If no function call, break
break
# Return
return new_messages
####################################################################################################
####################################################################################################
### EXAMPLE ###
# Create PromptBranches
class BranchA(PromptBranch):
header = "You love icecream, but you only like vanilla icecream."
footer = "If you choose to respond make sure you mention icecream."
description = "A Branch to talk about icecream."
children = {
'BranchB': 'If someone mentions anything fancy, be sure to switch to this branch.',
'BranchC': None,
}
class BranchB(PromptBranch):
header = "You love fine wines, but only if they are over 10 years old."
footer = "If you choose to respond make sure you mention wine."
description = "A Branch to talk about wine."
children = {
'BranchA': None,
'BranchC': None,
}
class BranchC(PromptBranch):
header = "You love going to the beach all the time no matter what."
footer = "If you choose to respond make sure you mention that you love the beach."
description = "A Branch to talk about the beach."
children = {
'BranchA': None,
'BranchB': None,
}
# Create PromptTree
class MyPromptTree(PromptTree):
preamble = "You are an AI who is obsessed with a few things."
first_branch = 'BranchA'
all_branches = {
'BranchA': BranchA,
'BranchB': BranchB,
'BranchC': BranchC,
}
### CONVERSATION ###
# Initialize the AI
llm = ChatOpenAI(model="gpt-3.5-turbo-0613")
tree = MyPromptTree()
agent = TreeAgent(tree, llm)
# Create sample conversation
messages = []
while True:
# Human input
user_message = input("You: ")
messages += [HumanMessage(content=user_message)]
# AI response
new_messages = agent(messages)
for m in new_messages:
print("AI:", m)
messages += new_messages
```
While this may not be a perfect way to go about things, it does demonstrate that with a relatively small amount of code we can work with existing LangChain arcitecture to implement a toy model. I think that with a little bit of work this could be made into something very useful.
I would love to learn more about if/how I can help contribute to incorporate this.
|
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| 1,871,851,733 |
PR_kwDOIPDwls5ZC57u
| 9,931 |
bump 276
|
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PR_kwDOIPDwls5ZCvmw
| 9,930 |
fix pydantic import
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I_kwDOIPDwls5vkKd_
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Issue: ParentDocumentRetriever is slow with FAISS because add_documents uses embed_query without batching
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[] | null | 7 | 2023-08-29T14:04:47 | 2023-11-14T03:53:51 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I wanted to use the new `ParentDocumentRetriever` but found the indexing extremely slow. I think the reason
is this line here https://github.com/langchain-ai/langchain/blob/e80834d783c6306a68df54e6251d9fc307aee87c/libs/langchain/langchain/retrievers/parent_document_retriever.py#L112
the `add_documents` for FAISS calls the `embed_texts` function in a list comprehension here https://github.com/langchain-ai/langchain/blob/e80834d783c6306a68df54e6251d9fc307aee87c/libs/langchain/langchain/vectorstores/faiss.py#L166
This only embeds a single chunk of text at a time which is really slow, especially when using OpenAIEmbeddings. Replacing this with a call to OpenAIEmbeddings.embed_documents(docs) would result in a huge speedup as it batches things up per API call (default batch size of a 1000).
I replaced the `self.vectorstore.add_documents(docs)` with
```python
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
embeddings = OpenAIEmbeddings().embed_documents([doc.page_content for doc in docs])
self.vectorstore._FAISS__add(texts, embeddings, metadatas)
```
But a more general solution would be needed where because on initialisation only the `embed_function` is stored and not the underlying embedding model itself.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5ZCgHO
| 9,928 |
fix a link name format to the dependents document
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[] | null | 1 | 2023-08-29T13:55:58 | 2023-08-29T22:20:06 | 2023-08-29T22:20:06 |
CONTRIBUTOR
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