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Makefile changes fix that.
`__getattr__` changes allow import of all files, but raise error when accessing anything from the module.
file deletions were corresponding libs change from #14559 | {
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- **Issue:** NA
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- **Tag maintainer:** @prakharg-msft @gdyre
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* [docs/docs/integrations/llms/azure_ml.ipynb](https://github.com/santiagxf/langchain/blob/santiagxf/azureml-endpoints-paygo-community/docs/docs/integrations/chat/azureml_endpoint.ipynb)
* [docs/docs/integrations/chat/azureml_chat_endpoint.ipynb](https://github.com/santiagxf/langchain/blob/santiagxf/azureml-endpoints-paygo-community/docs/docs/integrations/chat/azureml_chat_endpoint.ipynb)
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- **Twitter handle:** @lkuligin
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@baskaryan, @eyurtsev, @hwchase17.
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Hi,
I have this issue when working with more than 1 table using the Llama2 model. Let's say table 1 has values A, B, C, and table 2 has values X, Y, Z. When I run the query, it often gives me an error that it creates the query but assumes that table 1 has values X, Y, Z, and it mixes them up. Any suggestions on how to avoid this? I'm trying to solve it with prompts, but the model seems to ignore instructions in these cases.
### Suggestion:
_No response_ | {
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] | closed | false | null | [] | null | 8 | 2023-12-11T18:30:31 | 2023-12-22T09:32:06 | 2023-12-16T22:31:01 | NONE | null | ### System Info
Name: langchain
Version: 0.0.348
Name: PyGithub
Version: 2.1.1
### Who can help?
@hwchase17
@agola11
```
Traceback (most recent call last):
File "/Users/mac/Dev Projects/Chainlit_qa/test.py", line 12, in <module>
github = GitHubAPIWrapper(
^^^^^^^^^^^^^^^^^
File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/pydantic/v1/main.py", line 339, in __init__
values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/pydantic/v1/main.py", line 1102, in validate_model
values = validator(cls_, values)
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/langchain/utilities/github.py", line 69, in validate_environment
installation = gi.get_installations()[0]
~~~~~~~~~~~~~~~~~~~~~~^^^
File "/Users/mac/Dev Projects/Chainlit_qa/myenv/lib/python3.11/site-packages/github/PaginatedList.py", line 62, in __getitem__
return self.__elements[index]
~~~~~~~~~~~~~~~^^^^^^^
IndexError: list index out of range
```
### 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
Run basic python file:
```
from langchain.agents.agent_toolkits.github.toolkit import GitHubToolkit
from langchain.utilities.github import GitHubAPIWrapper
github = GitHubAPIWrapper()
toolkit = GitHubToolkit.from_github_api_wrapper(github)
tools = toolkit.get_tools()
```
### Expected behavior
Fetching github repository information | {
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```python
model: ChatVertexAI
chat_generation = model.generate([prompt.format_messages()]).generations[0][0]
grounding_metadata = chat_generation.generation_info["grounding_metadata"]
``` | {
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] | open | false | null | [] | null | 1 | 2023-12-11T18:06:33 | 2023-12-11T18:08:46 | null | CONTRIBUTOR | null | ### Feature request
As of `langchain==0.0.348` in [`ChatVertexAI` here](https://github.com/langchain-ai/langchain/blob/v0.0.348/libs/langchain/langchain/chat_models/vertexai.py#L187-L191):
1. `vertexai.language_models.ChatSession.send_message` returns a `vertexai.language_models.MultiCandidateTextGenerationResponse` response
2. `ChatVertexAI` throws out the response's `grounding_metadata`, only preserving the `text`
The request is to not discard this useful metadata
### Motivation
I am trying to use the `response.grounding_metadata.citations` in my own code
### Your contribution
I will open a PR to make this:
```python
generations = [
ChatGeneration(
message=AIMessage(content=r.text),
generation_info=r.raw_prediction_response,
)
for r in response.candidates
]
```
So the raw response remains accessible | {
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] | open | false | null | [] | null | 2 | 2023-12-11T16:25:09 | 2024-01-03T08:12:24 | null | NONE | null | ### System Info
Langchain 0.0.331, macOS Monterey, Python 3.10.9
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import UnstructuredHTMLLoader
loader = UnstructuredHTMLLoader("https://www.sec.gov/ix?doc=/Archives/edgar/data/40987/000004098720000010/gpc-12312019x10k.htm")
documents = loader.load()
FileNotFoundError: [Errno 2] No such file or directory: 'https://www.sec.gov/ix?doc=/Archives/edgar/data/40987/000004098720000010/gpc-12312019x10k.htm
### Expected behavior
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Deprecating tql_query parameter and fixing deeplake tests
Issue:
previously we used to use `tql_query` as a parameter to specify tql query during the search. But later we decided to change that to `tql`. In the PR we are adding deprecation warning as well as, fixing some failing tests and adding the check during search to check for unsupported parameters.
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- **Description:** There is a bug in RedisNum filter that filter towards value 0 will be parsed as "*". This is a fix to it.
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* Fixes Hybrid search normalization (before we didn't normalize vector similarity)
* Remove special lucene characters from full text index queries (fixes https://github.com/langchain-ai/langchain/issues/14232)
cc @jexp | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14538 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14538/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14538/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14538/events | https://github.com/langchain-ai/langchain/issues/14538 | 2,035,763,317 | I_kwDOIPDwls55V0h1 | 14,538 | impossible to connect to PowerBI Datasets even after providing all the information | {
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] | open | false | null | [] | null | 1 | 2023-12-11T14:07:35 | 2023-12-11T14:17:04 | null | NONE | null | ### System Info
Langchain version = 0.0.344
Python version = 3.11.5
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
here is my code. i am unable to connect to powerbi dataset.
from langchain.agents.agent_toolkits import PowerBIToolkit
from langchain.utilities.powerbi import PowerBIDataset
from azure.identity import ClientSecretCredential
from azure.core.credentials import TokenCredential
# Create an instance of the language model (llm)
toolkit = PowerBIToolkit(
powerbi=PowerBIDataset(
dataset_id="",
table_names=['WinShareTable'],
credential=ClientSecretCredential(
client_id=",
client_secret='',
tenant_id=""
)
),
llm=llm
)
Either getting field "credential" not yet prepared so type is still a ForwardRef, you might need to call PowerBIDataset.update_forward_refs() error or
Tokencredentials error
### Expected behavior
Should allow to connect to PowerBi datasets | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14536 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14536/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14536/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14536/events | https://github.com/langchain-ai/langchain/pull/14536 | 2,035,741,068 | PR_kwDOIPDwls5hr4sK | 14,536 | updated streaming chunk on_llm_new_token in vertexai | {
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] | closed | false | null | [] | null | 2 | 2023-12-11T13:57:08 | 2024-01-02T01:05:38 | 2024-01-02T01:05:37 | NONE | null | - **Description:** We have made changes to fix the issue in streaming chunks in vertexAI,
- **Issue:** Python app is getting crashed when trying to stream response in vertexAI,
- **Dependencies:** No dependency,
- Please help with the review @baskaryan @lkuligin @hwchase17 ,
- **Twitter handle:** https://twitter.com/shrinitg
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https://api.github.com/repos/langchain-ai/langchain/issues/14535 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14535/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14535/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14535/events | https://github.com/langchain-ai/langchain/pull/14535 | 2,035,606,803 | PR_kwDOIPDwls5hra_a | 14,535 | remove optional from ids param | {
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] | closed | false | null | [] | null | 2 | 2023-12-11T12:47:20 | 2023-12-13T00:47:43 | 2023-12-13T00:47:43 | CONTRIBUTOR | null | delete() requires ids to remove documents from the db.
Remove the optional type hinting for ids param.
fixed #14409
@efriis
@hwchase17
@baskaryan
Do you think we can add a separate function which remove's all documents without ids?
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https://api.github.com/repos/langchain-ai/langchain/issues/14534 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14534/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14534/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14534/events | https://github.com/langchain-ai/langchain/issues/14534 | 2,035,602,125 | I_kwDOIPDwls55VNLN | 14,534 | Stable release 11.4.0 of azure.search.documents.models not compatible with latest langchain version -> class Vector gone | {
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<div type='discussions-op-text'>
<sup>Originally posted by **yallapragada** November 12, 2023</sup>
I am testing a simple RAG implementation with Azure Cognitive Search.
I am seeing a "cannot import name 'Vector' from azure.search.documents.models" error when I invoke my chain. Origin of my error is line 434 in lanchain/vectorstores/azuresearch.py (from azure.search.documents.models import Vector)
this is the relevant code snippet, I get the import error when I execute rag_chain.invoke(question)
from langchain.schema.runnable import RunnablePassthrough
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models.azure_openai import AzureChatOpenAI
question = "my question.."
-- vector_store is initialized using AzureSearch(), not including that snippet here --
retriever = vector_store.as_retriever()
template = '''
Answer the question based on the following context:
{context}
Question: {question}
'''
prompt = ChatPromptTemplate.from_template(template=template)
llm = AzureChatOpenAI( deployment_name='MY_DEPLOYMENT_NAME', model_name='MY_MODEL', openai_api_base=MY_AZURE_OPENAI_ENDPOINT, openai_api_key=MY_AZURE_OPENAI_KEY, openai_api_version='2023-05-15', openai_api_type='azure' )
rag_chain = {'context' : retriever, 'question' : RunnablePassthrough} | prompt | llm
rag_chain.invoke(question)
--------------
my package versions
langchain==0.0.331
azure-search-documents==11.4.0b11
azure-core==1.29.5
openai==0.28.1</div> | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14533 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14533/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14533/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14533/events | https://github.com/langchain-ai/langchain/pull/14533 | 2,035,570,776 | PR_kwDOIPDwls5hrS-B | 14,533 | remove optional from ids param | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14532 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14532/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14532/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14532/events | https://github.com/langchain-ai/langchain/issues/14532 | 2,035,556,970 | I_kwDOIPDwls55VCJq | 14,532 | DOC: Please add stronger contrast or labeling between notebook input and output blocks | {
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] | open | false | null | [] | null | 2 | 2023-12-11T12:20:45 | 2023-12-25T06:31:10 | null | NONE | null | ### Issue with current documentation:
Page: https://python.langchain.com/docs/modules/memory/
In dark mode, there is very little contrast between inputs and outputs. Especially for pages imported from Jupyter notebooks, it can be really confusing figuring out which code blocks are safe to test in a `.py` function and which code blocks are intended for use in Jupyter.
Adding in explicit contrast or labeling between input and output blocks would be helpful.
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14531 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14531/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14531/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14531/events | https://github.com/langchain-ai/langchain/issues/14531 | 2,035,547,273 | I_kwDOIPDwls55U_yJ | 14,531 | Support Weaviate client v4 | {
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] | open | false | null | [] | null | 1 | 2023-12-11T12:15:23 | 2023-12-11T12:17:44 | null | NONE | null | ### Feature request
Weaviate has released a beta version of their [python client v4](https://weaviate.io/developers/weaviate/client-libraries/python) which seems to be more robust compared to v3. It follows a different structure but allows for more versatility and better error handling when it comes to queries. I think it would be great to add support in langchain for the new client.
### Motivation
I was using the v3 client (without langchain) to batch import data into Weaviate. It worked, but it was slower than I expected and also resulted in errors that I was not able to manage easily. The new v4 client supports their new [gRPC API](https://weaviate.io/developers/weaviate/api/grpc) which outperforms the traditional REST API that v3 is using.
### Your contribution
I will start creating some custom functions using Weaviate's new client to test its reliability. If I don't encounter any serious errors, I'll try to find the time and create a PR to add support for it in langchain. I think that support for both v3 and v4 should exist, at least until v4 becomes stable. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14530 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14530/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14530/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14530/events | https://github.com/langchain-ai/langchain/pull/14530 | 2,035,534,122 | PR_kwDOIPDwls5hrK2p | 14,530 | Added support for new Google GenerativeAI models | {
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- **Description:** added support for new Google GenerativeAI models
- **Twitter handle:** lkuligin
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https://api.github.com/repos/langchain-ai/langchain/issues/14528 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14528/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14528/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14528/events | https://github.com/langchain-ai/langchain/pull/14528 | 2,035,483,272 | PR_kwDOIPDwls5hq_aL | 14,528 | Update cohere provider docs | {
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<img width="1401" alt="image" src="https://github.com/langchain-ai/langchain/assets/144115527/9e8ba3d9-24ff-4584-9da3-2c9b60e7e624"> | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14526 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14526/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14526/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14526/events | https://github.com/langchain-ai/langchain/issues/14526 | 2,035,343,197 | I_kwDOIPDwls55UN9d | 14,526 | b'{"status":{"error":"Wrong input: Default vector params are not specified in config"},"time":0.00036507}' | {
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] | open | false | null | [] | null | 1 | 2023-12-11T10:30:41 | 2023-12-11T10:37:51 | null | NONE | null | ### System Info
langchain==0.0.348
wsl
python 3.10.2
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm looking to run a simple RAG using Qdrant pre instantiated with data (something like the [link from the docs](https://colab.research.google.com/drive/19RxxkZdnq_YqBH5kBV10Rt0Rax-kminD?usp=sharing#scrollTo=fvCHMT73SmKi)) but its giving me the error in the title.
I'm posting it here since the error seems to be on langchain side. not qdrant side.
Ive tried various ways of using langchain to connect to qdrant but it always ends up with this error.
This happens even if i use the deprecated VectorDBQA or RetrievalQA
```
from qdrant_client import QdrantClient
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Qdrant
QDRANT_ENDPOINT = "localhost"
MODEL_TO_USE = 'BAAI/bge-base-en-v1.5'
collection_name = "collection"
client = QdrantClient(QDRANT_ENDPOINT, port=6333, timeout=120)
embeddings = HuggingFaceEmbeddings(model_name=MODEL_TO_USE)
qdrant = Qdrant(client, collection_name, embeddings)
qdrant.similarity_search("middle east")
```
gives
```
>>> qdrant.similarity_search("middle east")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 274, in similarity_search
results = self.similarity_search_with_score(
File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 350, in similarity_search_with_score
return self.similarity_search_with_score_by_vector(
File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 595, in similarity_search_with_score_by_vector
results = self.client.search(
File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/qdrant_client.py", line 340, in search
return self._client.search(
File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/qdrant_remote.py", line 472, in search
search_result = self.http.points_api.search_points(
File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api/points_api.py", line 1388, in search_points
return self._build_for_search_points(
File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api/points_api.py", line 636, in _build_for_search_points
return self.api_client.request(
File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api_client.py", line 74, in request
return self.send(request, type_)
File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api_client.py", line 97, in send
raise UnexpectedResponse.for_response(response)
qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request)
Raw response content:
b'{"status":{"error":"Wrong input: Default vector params are not specified in config"},"time":0.000118885}'
```
Using this as a retriever also fails, which is what i need it for.
Testing with qdrant alone works.
```
client.query(collection_name,"middle east") # using qdrant itself works
```
### Expected behavior
For the queries to be fetched and not throw this error | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14525 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14525/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14525/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14525/events | https://github.com/langchain-ai/langchain/pull/14525 | 2,035,279,943 | PR_kwDOIPDwls5hqSdN | 14,525 | fix doctran translation async error | {
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] | closed | false | null | [] | null | 3 | 2023-12-11T09:58:25 | 2023-12-28T16:05:36 | 2023-12-28T16:05:36 | CONTRIBUTOR | null | Currenly [Doctran](https://github.dev/psychic-api/doctran) doesn't have async support.
removing from langchain as well, will run it sync.
fixed #14437
@efriis
@baskaryan
@hwchase17
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https://api.github.com/repos/langchain-ai/langchain/issues/14524 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14524/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14524/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14524/events | https://github.com/langchain-ai/langchain/pull/14524 | 2,035,164,760 | PR_kwDOIPDwls5hp5Hw | 14,524 | Add the collection_description parameter to Milvus | {
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] | closed | false | null | [] | null | 4 | 2023-12-11T09:02:48 | 2024-01-03T00:49:24 | 2024-01-03T00:28:02 | CONTRIBUTOR | null | Because Milvus' collection_name doesn't support UFT8 characters in other languages, I want the `collection_descriotion`.
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https://api.github.com/repos/langchain-ai/langchain/issues/14523 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14523/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14523/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14523/events | https://github.com/langchain-ai/langchain/issues/14523 | 2,035,156,450 | I_kwDOIPDwls55TgXi | 14,523 | When the self-deployed chatglm3 model is invoked based on the openai API specification and create_sql_agent is used to query the first three rows of the data table, the output format is reported to be incorrect. But there are no formatting errors with qwen. How do I fix chatglm3 | {
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} | [] | open | false | null | [] | null | 1 | 2023-12-11T08:58:58 | 2023-12-11T09:07:43 | null | NONE | null | ### System Info
langchain=0.0.348
openai=0.28.1
### 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
model= "chatglm3"
llm = ChatOpenAI(model_name=model,openai_api_key=api_key,openai_api_base=api_url)
db = SQLDatabase.from_uri("mysql+pymysql://.....")
toolkit = SQLDatabaseToolkit(db=db, llm=llm,use_query_checker=True)
agent_executor = create_sql_agent( llm=llm,toolkit=toolkit, verbose=True,agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,handle_parsing_errors=False
)
content=agent_executor.run("Look at the structure of the table ads_pm_empl_count first, then give the first three rows of data")
print(content)
<img width="875" alt="联想截图_20231211165355" src="https://github.com/langchain-ai/langchain/assets/73893296/8fcee38e-0235-44c5-8454-6fd368351488">
### Expected behavior
How do I fix this output format problem | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14522 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14522/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14522/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14522/events | https://github.com/langchain-ai/langchain/issues/14522 | 2,035,138,288 | I_kwDOIPDwls55Tb7w | 14,522 | 1 | {
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langchain=0.0.348
python=3.9
openai=0.28.1
### 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
![Uploading 联想截图_20231211165355.png…]()
model= "chatglm3"
#llm = OpenAI(model_name=model,openai_api_key=api_key,openai_api_base=api_url)
llm = ChatOpenAI(model_name=model,openai_api_key=api_key,openai_api_base=api_url)
db = SQLDatabase.from_uri("mysql+pymysql://。。。。。")
toolkit = SQLDatabaseToolkit(db=db, llm=llm,use_query_checker=True)
#memory = ConversationBufferMemory(memory_key ="chat_history ")
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=False
)
# content=agent_executor.output_schema.schema()
content=agent_executor.run("Look at the structure of the table ads_pm_empl_count first, then give the first three rows of data")
print(content)
### Expected behavior
How do I fix this output format problem | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14521 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14521/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14521/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14521/events | https://github.com/langchain-ai/langchain/issues/14521 | 2,035,123,971 | I_kwDOIPDwls55TYcD | 14,521 | [Maybe Bug] `StructuredChatAgent` raises `ValueError` with a `StructuredTool` which has an argument which defaults to a `dict` default value | {
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] | open | false | null | [] | null | 3 | 2023-12-11T08:41:13 | 2023-12-11T10:46:26 | null | CONTRIBUTOR | null | ### System Info
OS == Windows 11
Python == 3.10.11
Langchain == 0.0.348
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```bash
Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr 5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from dotenv import load_dotenv
>>> load_dotenv()
True
>>> from langchain.agents import AgentType, initialize_agent
>>> from langchain.tools import StructuredTool
>>> from langchain.chat_models import ChatOpenAI
>>> llm = ChatOpenAI(model='gpt-3.5-turbo')
>>> def func0(a: int, b: int) -> int:
... return a+b
...
>>> def func1(a: int, b: int, d: dict[str, int]={}) -> int:
... print(d)
... return a+b
...
>>> def func2(a: int, b: int, d: dict[str, int]={'c': 0}) -> int:
... print(d)
... return a+b
...
>>> tool0 = StructuredTool.from_function(func0, name=func0.__name__, description='a+b')
>>> tool1 = StructuredTool.from_function(func1, name=func1.__name__, description='a+b')
>>> tool2 = StructuredTool.from_function(func2, name=func2.__name__, description='a+b')
>>> agent0 = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=[tool0], llm=llm)
>>> agent1 = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=[tool1], llm=llm)
>>> agent2 = initialize_agent(agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, tools=[tool2], llm=llm)
>>> agent0.run('hello')
'Hi there! How can I assist you today?'
>>> agent0.invoke(dict(input='hello'))
{'input': 'hello', 'output': 'Hi there! How can I assist you today?'}
>>> agent1.run('hello')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 507, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__
inputs = self.prep_inputs(inputs)
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 435, in prep_inputs
raise ValueError(
ValueError: A single string input was passed in, but this chain expects multiple inputs ({'', 'input'}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({'foo': 1, 'bar': 2})`
>>> agent1.invoke(dict(input='hello'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 89, in invoke
return self(
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__
inputs = self.prep_inputs(inputs)
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 445, in prep_inputs
self._validate_inputs(inputs)
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 197, in _validate_inputs
raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {''}
>>> agent2.run('hello')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 507, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__
inputs = self.prep_inputs(inputs)
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 435, in prep_inputs
raise ValueError(
ValueError: A single string input was passed in, but this chain expects multiple inputs ({"'c'", 'input'}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({'foo': 1, 'bar': 2})`
>>> agent2.invoke(dict(input='hello'))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 89, in invoke
return self(
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 288, in __call__
inputs = self.prep_inputs(inputs)
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 445, in prep_inputs
self._validate_inputs(inputs)
File "D:\workspace\agi-baby\venv\lib\site-packages\langchain\chains\base.py", line 197, in _validate_inputs
raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {"'c'"}
```
### Expected behavior
Given a `StructuredTool` which has an argument which defaults to a `dict` value, `StructuredChatAgent` with the tool should work.
In the above reproduction codes, `agent1` and `agent2` should works as `agent0` works.
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https://api.github.com/repos/langchain-ai/langchain/issues/14520 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14520/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14520/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14520/events | https://github.com/langchain-ai/langchain/issues/14520 | 2,035,027,147 | I_kwDOIPDwls55TAzL | 14,520 | StructuredChatAgent would go wrong when the input contain some code such as protobuf. | {
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] | open | false | null | [] | null | 3 | 2023-12-11T07:44:54 | 2023-12-11T08:39:42 | null | NONE | null | ### System Info
LangChain: 0.0.311
Python: 3.11
OS: macOS 11.6
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Thought: The user is asking for help implementing CRUD operations for a specific table in a MySQL database using the Go language. This is a programming task so I will use the programmer_agent tool to help with this.
Action:
```
{
"action": "programmer_agent",
"action_input": {
"task": {
"title": "Implement CRUD operations for limited_relationships_config table in MySQL using Go",
"description": "Write functions for adding, updating, deleting, and retrieving limited relationships configurations in a MySQL database using Go. The operations will be performed on the `limited_relationships_config` table.",
"type": "code"
}
}
}
```
### Expected behavior
Thought: The user is asking for help implementing CRUD operations for a specific table in a MySQL database using the Go language. This is a programming task so I will use the programmer_agent tool to help with this.
Action:
```
{
"action": "programmer_agent",
"action_input": {
"task": "Implement CRUD operations for limited_relationships_config table in MySQL using Go"
}
}
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14519 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14519/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14519/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14519/events | https://github.com/langchain-ai/langchain/issues/14519 | 2,035,017,496 | I_kwDOIPDwls55S-cY | 14,519 | how to use gpt2 with custom promt? | {
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] | open | false | null | [] | null | 1 | 2023-12-11T07:38:08 | 2023-12-11T08:36:52 | null | NONE | null | ### Feature request
i want to use gpt2 for text genration & want to control the llm
### Motivation
gpt2 is smaller version & its is best for next-word prediction
### Your contribution
want to use below code for loading gpt2 model
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = 'gpt2'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
prompt = 'want to do more '
config1 = {
'num_beams': 3, # Number of beams for beam search
'do_sample': True, # Whether to do sampling or not
'temperature': 0.6 # The value used to module the next token probabilities
}
config2 = {
'penalty_alpha': 0.6, # The penalty alpha for contrastive search
'top_k': 6 # The number of highest probability vocabulary tokens to keep for top-k-filtering
}
inputs = tokenizer(prompt, return_tensors='pt')
output = model.generate(**inputs, **config1) # Here, you should choose config1 or config2
result = tokenizer.decode(output[0], skip_special_tokens=True)
print("------->>>>.",result)
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14518 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14518/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14518/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14518/events | https://github.com/langchain-ai/langchain/issues/14518 | 2,034,867,869 | I_kwDOIPDwls55SZ6d | 14,518 | Is 'collection_name' required when initializing 'PGVector'? | {
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] | open | false | null | [] | null | 1 | 2023-12-11T05:36:15 | 2023-12-11T05:41:26 | null | NONE | null | ### System Info
```langchain = "^0.0.345"```
I want to embed and store multiple documents in PGVector and RAG query the DB.
When saving documents, I am specifying a collection_name for each document.
(For example, if I have 3 documents, I have 3 collection_names)
Is it possible to separate collections like this?
Also, is the collection_name required when connecting to PGVector?
### 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
db1 = PGVector.from_documents(
embedding=embed,
documents=documents,
collection_name="my doc 1",
connection_string=CONNECTION_STRING
)
db2 = PGVector.from_documents(
embedding=embed,
documents=documents,
collection_name="my doc 2",
connection_string=CONNECTION_STRING
)
# after, To query the DB where the document is stored.
db_connection = PGVector(
embedding=embed,
documents=documents,
collection_name="my doc 2", # I don't want to specify a collection_name.
connection_string=CONNECTION_STRING
)
### Expected behavior
I want to connect to the PGVector only the first time, and then use that session to query the collection_name (1 document in my case). | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14517 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14517/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14517/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14517/events | https://github.com/langchain-ai/langchain/issues/14517 | 2,034,861,990 | I_kwDOIPDwls55SYem | 14,517 | Breaking of pattern in `from_document` function | {
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] | open | false | null | [] | null | 1 | 2023-12-11T05:30:25 | 2023-12-11T05:32:41 | null | NONE | null | https://github.com/langchain-ai/langchain/blob/c0f4b95aa9961724ab4569049b4c3bc12ebbacfc/libs/langchain/langchain/vectorstores/chroma.py#L742
This function breaks the pattern of how the `embedding_function` is referenced by just calling it `embedding`.
Small issue but definitely makes it a bit confusing to navigate without diving into the code/docs.
Happy to PR it in if worthwhile | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14516 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14516/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14516/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14516/events | https://github.com/langchain-ai/langchain/pull/14516 | 2,034,770,535 | PR_kwDOIPDwls5hoiTS | 14,516 | RunnableWithMessageHistory: Fix input schema | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14515 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14515/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14515/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14515/events | https://github.com/langchain-ai/langchain/issues/14515 | 2,034,721,756 | I_kwDOIPDwls55R2Pc | 14,515 | Qdrant retriever with existing data leads to pydantic.error_wrappers.ValidationError: 1 validation error for Document page_content none is not an allowed value (type=type_error.none.not_allowed) | {
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] | open | false | null | [] | null | 5 | 2023-12-11T02:50:24 | 2024-01-11T08:35:39 | null | NONE | null | ### System Info
Python 3.10.2
langchain version 0.0.339
WSL
### Who can help?
@hwchase17
@agola11
### Information
- [x ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am using Qdrant on docker with information preloaded. However im unable to get it to search
```
MODEL_TO_USE = 'all-mpnet-base-v2'
client = QdrantClient(QDRANT_ENDPOINT, port=6333, timeout=120)
embeddings = HuggingFaceEmbeddings(model_name=MODEL_TO_USE)
qdrant = Qdrant(client, collection_name, embeddings)
conversation_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=qdrant.as_retriever(search_kwargs={'k': 5}),
get_chat_history=lambda h : h,
memory=memory
)
# im using a streamlit frontend
import streamlit as st
st.session_state.conversation = conversation_chain
# during chat
result = st.session_state.conversation({
"question": user_query,
"chat_history": st.session_state['chat_history']})
```
Result would then lead to the error.
### Expected behavior
Chat is supposed to work instead of getting
```
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for Document
page_content
none is not an allowed value (type=type_error.none.not_allowed)
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14514 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14514/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14514/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14514/events | https://github.com/langchain-ai/langchain/issues/14514 | 2,034,717,889 | I_kwDOIPDwls55R1TB | 14,514 | DOC: Could load GGUF models from https | {
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] | open | false | null | [] | null | 21 | 2023-12-11T02:45:28 | 2023-12-12T12:06:19 | null | NONE | null | ### Issue with current documentation:
_No response_
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14513 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14513/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14513/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14513/events | https://github.com/langchain-ai/langchain/issues/14513 | 2,034,583,189 | I_kwDOIPDwls55RUaV | 14,513 | Customize how messages are formatted in MessagesPlaceholder | {
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] | open | false | null | [] | null | 1 | 2023-12-10T23:33:17 | 2023-12-10T23:35:20 | null | NONE | null | ### Feature request
Customize how messages are formatted in MessagesPlaceholder
Currently, history messages are always format to:
'''
Human: ...
AI: ...
'''
Popular chat fine-tunings use all sorts of different formats.
Example:
'''
<|im_start|>user
...<|im_end|>
<|im_start|>assistant
...<|im_end|>
'''
There's currently no way to change how history messages are prompted.
### Motivation
I wanted to use langchain to make chatbots. Currently that use case is not well supported.
### Your contribution
Here's how I got it to work. I have to manually parse the history in a previous step.
<img width="683" alt="image" src="https://github.com/langchain-ai/langchain/assets/2053475/15fe31e2-cc23-47f2-9d0c-d5670f22768a">
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https://api.github.com/repos/langchain-ai/langchain/issues/14512 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14512/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14512/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14512/events | https://github.com/langchain-ai/langchain/pull/14512 | 2,034,536,844 | PR_kwDOIPDwls5hnwqK | 14,512 | fix: updating internal batch size for gradient embeddings. | {
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] | open | false | null | [] | null | 2 | 2023-12-10T21:15:32 | 2023-12-11T19:44:54 | null | CONTRIBUTOR | null | background: i am author of the gradient integrations.
Issue:
Gradient.ai accepts batch_sizes until 100. This integration sets up to 128, which leads to errors when > 101 embeddings are requested in a function call.
Fix: This PR sets the size per Request to 64.
```bash
Cell In[5], [line 7](vscode-notebook-cell:?execution_count=5&line=7)
[5](vscode-notebook-cell:?execution_count=5&line=5) import timeit
[6](vscode-notebook-cell:?execution_count=5&line=6) start = timeit.default_timer()
----> [7](vscode-notebook-cell:?execution_count=5&line=7) documents_embedded = embeddings.embed_documents(documents)
[8](vscode-notebook-cell:?execution_count=5&line=8) end = timeit.default_timer()
[10](vscode-notebook-cell:?execution_count=5&line=10) print("Query result:", query_result)
File [~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:103](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:103), in GradientEmbeddings.embed_documents(self, texts)
[94](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:94) def embed_documents(self, texts: List[str]) -> List[List[float]]:
[95](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:95) """Call out to Gradient's embedding endpoint.
[96](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:96)
[97](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:97) Args:
(...)
[101](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:101) List of embeddings, one for each text.
[102](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:102) """
--> [103](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:103) embeddings = self.client.embed(
[104](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:104) model=self.model,
[105](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:105) texts=texts,
[106](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:106) )
[107](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:107) return embeddings
File [~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:331](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:331), in TinyAsyncGradientEmbeddingClient.embed(self, model, texts)
[329](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:329) else:
[330](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:330) with ThreadPoolExecutor(32) as p:
--> [331](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:331) embeddings_batch_perm = list(p.map(*map_args))
[333](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:333) embeddings_perm = self._unbatch(embeddings_batch_perm)
[334](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:334) embeddings = unpermute_func(embeddings_perm)
File [~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:621](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:621), in Executor.map.<locals>.result_iterator()
[618](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:618) while fs:
[619](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:619) # Careful not to keep a reference to the popped future
[620](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:620) if timeout is None:
--> [621](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:621) yield _result_or_cancel(fs.pop())
[622](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:622) else:
[623](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:623) yield _result_or_cancel(fs.pop(), end_time - time.monotonic())
File [~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:319](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:319), in _result_or_cancel(***failed resolving arguments***)
[317](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:317) try:
[318](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:318) try:
--> [319](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:319) return fut.result(timeout)
[320](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:320) finally:
[321](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/concurrent/futures/_base.py:321) fut.cancel()
...
[304](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:304) f"{response.status_code}: {response.text}"
[305](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:305) )
[306](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/michi/langchain/docs/docs/integrations/text_embedding/~/miniconda3/envs/hf_env/lib/python3.10/site-packages/langchain/embeddings/gradient_ai.py:306) return [e["embedding"] for e in response.json()["embeddings"]]
Exception: Gradient returned an unexpected response with status 400: {"message":"Invalid input: {\"detail\":[{\"type\":\"too_long\",\"loc\":[\"body\",\"texts\"],\"msg\"
```
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https://api.github.com/repos/langchain-ai/langchain/issues/14511 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14511/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14511/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14511/events | https://github.com/langchain-ai/langchain/issues/14511 | 2,034,526,238 | I_kwDOIPDwls55RGge | 14,511 | RunnableWithMessageHistory streaming bug | {
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] | open | false | null | [] | null | 10 | 2023-12-10T20:43:15 | 2023-12-29T19:27:13 | null | NONE | null | ### System Info
0.0.348, linux , python 3.11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL, ttl=600),
input_messages_key="inputs",
history_messages_key="history",
)
return chain_with_history
response = customer_support_invoke().stream(
{"inputs": userChats.inputs, "profile": userChats.profile},
config={"configurable": {"session_id": "123"}},
)
for s in response:
print(s.content, end="", flush=True)
```
if I streaming twice , it will raise an error "ValueError: Got unexpected message type: AIMessageChunk
"
### Expected behavior
streaming correctly | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14509 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14509/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14509/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14509/events | https://github.com/langchain-ai/langchain/pull/14509 | 2,034,520,774 | PR_kwDOIPDwls5hntiB | 14,509 | Added notebook tutorial on using Yellowbrick as a vector store with LangChain | {
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] | closed | false | null | [] | null | 1 | 2023-12-10T20:25:15 | 2023-12-13T00:59:06 | 2023-12-13T00:59:05 | CONTRIBUTOR | null | - **Description:** a notebook documenting Yellowbrick as a vector store usage
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https://api.github.com/repos/langchain-ai/langchain/issues/14508 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14508/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14508/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14508/events | https://github.com/langchain-ai/langchain/issues/14508 | 2,034,443,411 | I_kwDOIPDwls55QyST | 14,508 | Help me run this | {
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] | closed | false | null | [] | null | 8 | 2023-12-10T16:37:15 | 2023-12-11T04:49:51 | 2023-12-11T04:49:51 | NONE | null | ### Issue with current documentation:
import requests
from typing import Optional
from langchain.tools import StructuredTool
``` python
def post_message(url: str, body: dict, parameters: Optional[dict]=None) -> str:
"""Sends a POST request to the given url with the given body and parameters."""
result = requests.post(url, json=body, params=parameters)
return result.text
custom_tool = StructuredTool.from_function(post_message)
from langchain.agents import initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
tools = [custom_tool] # Add any tools here
llm = ChatOpenAI(temperature=0) # or any other LLM
agent_chain = initialize_agent(tools, llm, agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
```
How to run this and get the results if I have an api endpoint
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14507 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14507/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14507/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14507/events | https://github.com/langchain-ai/langchain/pull/14507 | 2,034,440,216 | PR_kwDOIPDwls5hndp9 | 14,507 | Move runnable context to beta | {
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] | closed | false | null | [] | null | 1 | 2023-12-10T16:28:41 | 2023-12-11T21:58:32 | 2023-12-11T21:58:31 | 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/langchain-ai/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. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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https://api.github.com/repos/langchain-ai/langchain/issues/14506 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14506/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14506/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14506/events | https://github.com/langchain-ai/langchain/pull/14506 | 2,034,425,460 | PR_kwDOIPDwls5hnasY | 14,506 | Add Connery Tool and Toolkit | {
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] | open | false | null | [] | null | 2 | 2023-12-10T15:50:26 | 2024-01-15T23:26:55 | null | NONE | 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.
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
## Summary
This PR implements the "Connery Action Tool" and "Connery Toolkit". Using them, you can integrate Connery actions into your LangChain agents and chains.
Connery is an open-source plugin infrastructure for AI.
With Connery, you can easily create a custom plugin with a set of actions and seamlessly integrate them into your LangChain agents and chains. Connery will handle the rest: runtime, authorization, secret management, access management, audit logs, and other vital features. Additionally, Connery and our community offer a wide range of ready-to-use open-source plugins for your convenience.
Learn more about Connery:
- GitHub: https://github.com/connery-io/connery-platform
- Documentation: https://docs.connery.io
- Twitter: https://twitter.com/connery_io
## TODOs
- [x] API wrapper
- [x] Integration tests
- [x] Connery Action Tool
- [x] Docs
- [x] Example
- [x] Integration tests
- [x] Connery Toolkit
- [x] Docs
- [x] Example
- [x] Formatting (`make format`)
- [x] Linting (`make lint`)
- [x] Testing (`make test`)
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https://api.github.com/repos/langchain-ai/langchain/issues/14505 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14505/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14505/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14505/events | https://github.com/langchain-ai/langchain/issues/14505 | 2,034,414,953 | I_kwDOIPDwls55QrVp | 14,505 | DOC: LLM Caching example using in-memory cache is unclear | {
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The in-memory cache section of the LLM Caching documentation shows uncached and subsequent cached responses that differ. The later examples show cached responses that are the same as the uncached response, which makes more sense.
### Idea or request for content:
The cached response under LLM Caching: In-memory should be "\n\nWhy couldn't the bicycle stand up by itself? It was...two tired!" | {
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[Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers) is an innovative toolkit to accelerate Transformer-based models on Intel platforms, in particular effective on 4th Intel Xeon Scalable processor [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html) (codenamed Sapphire Rapids). The toolkit provides the below key features:
* Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
* Advanced software optimizations and unique compression-aware runtime.
* Optimized Transformer-based model packages.
* [NeuralChat](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of plugins and SOTA optimizations.
* [Inference](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/llm/runtime/graph) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels.
This PR is an integration of weight only quantization feature with intel-extension-for-transformers.
Unit test is in lib/langchain/tests/integration_tests/llm/test_weight_only_quantization.py
The notebook is in docs/docs/integrations/llms/weight_only_quantization.ipynb.
The document is in docs/docs/integrations/providers/weight_only_quantization.mdx.
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<!-- Thank you for contributing to LangChain!
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The validate_template is now set to False by default while creating a PromptTemplate. So, I have made sure that those changes are now being reflected in this new validate.ipynb file where I have written codes by using both the cases, that are validate_template = True and validate_template = True.
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https://api.github.com/repos/langchain-ai/langchain/issues/14496 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14496/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14496/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14496/events | https://github.com/langchain-ai/langchain/pull/14496 | 2,034,041,381 | PR_kwDOIPDwls5hmMeH | 14,496 | feat: Yaml output parser | {
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] | closed | false | null | [] | null | 1 | 2023-12-09T20:28:59 | 2023-12-13T01:04:32 | 2023-12-13T01:04:32 | CONTRIBUTOR | null | ## Description
New YAML output parser as a drop-in replacement for the Pydantic output parser. Yaml is a much more token-efficient format than JSON, proving to be **~35% faster and using the same percentage fewer completion tokens**.
☑️ Formatted
☑️ Linted
☑️ Tested (analogous to the existing`test_pydantic_parser.py`)
The YAML parser excels in situations where a list of objects is required, where the root object needs no key:
```python
class Products(BaseModel):
__root__: list[Product]
```
I ran the prompt `Generate 10 healthy, organic products` 10 times on one chain using the `PydanticOutputParser`, the other one using the`YamlOutputParser` with `Products` (see below) being the targeted model to be created.
LLMs used were Fireworks' `lama-v2-34b-code-instruct` and OpenAI `gpt-3.5-turbo`. All runs succeeded without validation errors.
```python
class Nutrition(BaseModel):
sugar: int = Field(description="Sugar in grams")
fat: float = Field(description="% of daily fat intake")
class Product(BaseModel):
name: str = Field(description="Product name")
stats: Nutrition
class Products(BaseModel):
"""A list of products"""
products: list[Product] # Used `__root__` for the yaml chain
```
Stats after 10 runs reach were as follows:
### JSON
ø time: 7.75s
ø tokens: 380.8
### YAML
ø time: 5.12s
ø tokens: 242.2
Looking forward to feedback, tips and contributions! | {
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] | open | false | null | [] | null | 3 | 2023-12-09T17:55:13 | 2023-12-27T02:51:41 | null | NONE | null | - **Description:** Implement the _acall method of the class langchain.llms.gpt4all.GPT4All
- **Issue:** GPT4All chat error with async calls #5210
- **Dependencies:** no dependency
- **Tag maintainer:** @hwchase17
- **Twitter handle:** None
Okey, I tested locally.
I ran "make format". No issue.
I ran "make lint". I found this issue [How to fix error "cannot import name 'ModelMetaclass' from 'pydantic.main'"](https://stackoverflow.com/questions/76647518/how-to-fix-error-cannot-import-name-modelmetaclass-from-pydantic-main). I followed the answer pip install lightning==2.0.1 and the command ran without problem. I believe this is not related to the PR but I had to clarify this.
I ran "make test". No issue.
This is more of a fix for the existing default implementation of the _acall method inherited by the GPT4All class.
This is my first contribution ever in the open source world, so let me know if there is anything wrong.
1. Let me know if there is a need for adding unit tests and integration tests for LLMs or chains or callbacks. I read the existing tests and found out that they cover them in a generic manner.
2. I have a working example that showcases how to effectively use an asynchronous callback handler with the GPT4All and RetrievalQA. I did not find the directory docs/extra, should I put it in cookbooks?
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https://api.github.com/repos/langchain-ai/langchain/issues/14494 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14494/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14494/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14494/events | https://github.com/langchain-ai/langchain/issues/14494 | 2,033,925,929 | I_kwDOIPDwls55Oz8p | 14,494 | AmazonKendraRetriever: Error The provided QueryText has a character count of 1020, which exceeds the limit. | {
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] | open | false | null | [] | null | 5 | 2023-12-09T16:34:11 | 2023-12-10T10:32:35 | null | NONE | null | ### System Info
Langchain version: 0.0.348
Python version: 3.10.6
Platform: Linux
The issue is that when using Conversational Chain for with Kendra Retriever with memory, on any followup questions it gives this error.
My guess is somehow QueryText is getting more than the {question} value asked.
It works for first hit but for any follow up question (while memory is not empty) it gives this error.
### Who can help?
@hwchase17
@ago
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce error:
Create the retriever object:
from langchain.retrievers import AmazonKendraRetriever
retriever = AmazonKendraRetriever(
index_id=kendra_index_id,
top_k = 5
)
Then use it in Conversational Chain:
chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory,
verbose=True,
condense_question_prompt=chat_prompt,
combine_docs_chain_kwargs=dict(prompt=rag_prompt))
chain.run({"question":question})
Run at least twice, to get the error.
### Expected behavior
There shouldn't be any such error.
Question or QueryText is always less than 1000 characters. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14493 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14493/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14493/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14493/events | https://github.com/langchain-ai/langchain/pull/14493 | 2,033,896,613 | PR_kwDOIPDwls5hlt63 | 14,493 | Fix token_usage None issue in ChatOpenAI with local Chatglm2-6B | {
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] | closed | false | null | [] | null | 1 | 2023-12-09T15:36:41 | 2023-12-13T01:30:38 | 2023-12-13T01:30:38 | CONTRIBUTOR | null | When using local Chatglm2-6B by changing OPENAI_BASE_URL to localhost, the token_usage in ChatOpenAI becomes None. This leads to an AttributeError when trying to access token_usage.items().
This commit adds a check to ensure token_usage is not None before accessing its items. This change prevents the AttributeError and allows ChatOpenAI to work seamlessly with a local Chatglm2-6B model, aligning with the way it operates with the OpenAI API.
<!-- Thank you for contributing to LangChain!
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This PR intends to simplify the Qdrant async implementation by replacing the internal GRPC methods with the `QdrantAsyncClient` methods.
This is a backward compatible change with no additional steps required after merge. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14490 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14490/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14490/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14490/events | https://github.com/langchain-ai/langchain/pull/14490 | 2,033,844,531 | PR_kwDOIPDwls5hljjZ | 14,490 | Update OpenAI Default Model to 'gpt-3.5-turbo-instruct' | {
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] | closed | false | null | [] | null | 2 | 2023-12-09T13:11:14 | 2023-12-29T01:45:06 | 2023-12-29T01:45:06 | CONTRIBUTOR | null | As per the guidance provided on the OpenAI model documentation page (https://platform.openai.com/docs/models/gpt-3-5), the text-davinci-003 model is scheduled to be discontinued as of January 4, 2024.
In response to this upcoming change, I have updated the default model in the langchain.llms.openai.OpenAI class from 'text-davinci-003' to 'gpt-3.5-turbo-instruct', following the recommendations provided by OpenAI.
Additionally, I have also updated the modelname_to_contextsize() method to include the maximum token count for the new models(gpt-4-1106-preview, gpt-3.5-turbo-1106) | {
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**Tag maintainers:** @baskaryan | {
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- **Issue:** the issue # #13464 https://creditone.us.to/langchain-ai/langchain/issues/13464,
- **Dependencies:** None,
- **Twitter handle:** @frankybridman | {
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] | open | false | null | [] | null | 2 | 2023-12-09T12:19:40 | 2023-12-10T05:22:22 | null | NONE | null | ### Issue you'd like to raise.
I tried to work on SQL cutsom prompt, but it didn't work and is still giving the wrong sql queries . Here is the code : def process_user_input(user_input):
create_db()
input_db = SQLDatabase.from_uri('sqlite:///sample_db_2.sqlite')
llm_1 = OpenAI(temperature=0)
db_agent = SQLDatabaseChain.from_llm(llm = llm_1,
db = input_db,
verbose=True,)
chain = create_sql_query_chain(ChatOpenAI(temperature=0), input_db)
response = chain.invoke({"question": user_input})
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14486 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14486/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14486/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14486/events | https://github.com/langchain-ai/langchain/pull/14486 | 2,033,728,088 | PR_kwDOIPDwls5hlLS0 | 14,486 | DOC: model update in 'Using OpenAI Functions' docs | {
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I just update the openai functions docs to use the latest model (ex. gpt-3.5-turbo-1106)
https://python.langchain.com/docs/modules/chains/how_to/openai_functions
The reason is as follow:
After reviewing the OpenAI Function Calling official guide at https://platform.openai.com/docs/guides/function-calling, the following information was noted:
> "The latest models (gpt-3.5-turbo-1106 and gpt-4-1106-preview) have been trained to both detect when a function should be called (depending on the input) and to respond with JSON that adheres to the function signature more closely than previous models. With this capability also comes potential risks. We strongly recommend building in user confirmation flows before taking actions that impact the world on behalf of users (sending an email, posting something online, making a purchase, etc)."
CC: @efriis | {
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] | closed | false | null | [] | null | 4 | 2023-12-09T08:06:14 | 2023-12-10T04:17:57 | 2023-12-10T04:17:56 | NONE | null | ### System Info
I have retriever implementation like this
```
def get_vector_store(options: StoreOptions) -> VectorStore:
"""Gets the vector store for the given options."""
vector_store: VectorStore
embedding = get_embeddings()
store_type = os.environ.get("STORE")
if store_type == StoreType.QDRANT.value:
client = qdrant_client.QdrantClient(
url=os.environ["QDRANT_URL"],
prefer_grpc=True,
api_key=os.getenv("QDRANT_API_KEY", None),
)
vector_store = Qdrant(
client, collection_name=options.namespace, embeddings=embedding
)
# vector_store = Qdrant.from_documents([], embedding, url='http://localhost:6333', collection=options.namespace)
else:
raise ValueError("Invalid STORE environment variable value")
return vector_store
```
get-embeddings.py
```
return OllamaEmbeddings(base_url=f"host.docker.internal:11434", model="mistral")
```
```
knowledgebase: VectorStore = get_vector_store(StoreOptions("knowledgebase"))
async def get_relevant_docs(text: str, bot_id: str) -> Optional[str]:
try:
kb_retriever = knowledgebase.as_retriever(
search_kwargs={
"k": 3,
"score_threshold": vs_thresholds.get("kb_score_threshold"),
"filter": {"bot_id": bot_id},
},
)
result = kb_retriever.get_relevant_documents(text)
if result and len(result) > 0:
# Assuming result is a list of objects and each object has a page_content attribute
all_page_content = "\n\n".join([item.page_content for item in result])
return all_page_content
return None
except Exception as e:
logger.error(
"Error occurred while getting relevant docs",
incident="get_relevant_docs",
payload=text,
error=str(e),
)
return None
```
As long as i use chatgpt embeddings and chat models, i get the correct outputs. Once i switch to ollama, none of my retrievers are working.
I see the documents being ingested to qdrant, which means embeddings are working, but retrievers fail to retrieve any document
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
ss
### Expected behavior
retrievers should be able to fetch the documents from qdrant irrespective of embedding models being used | {
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] | closed | false | null | [] | null | 1 | 2023-12-09T07:11:28 | 2023-12-13T22:38:28 | 2023-12-13T22:38:28 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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] | open | false | null | [] | null | 7 | 2023-12-09T07:06:17 | 2024-01-02T18:57:53 | null | NONE | null | ### Feature request
It would be nice to write code for any generic LLM to use in a given chain, being able to specify its provider and parameters in a config file outside the source code.
Is there a built-in way to do so? What major roadblocks would you see in doing that?
### Motivation
Most examples that I see in the docs or over the Internet tend to specify from code whether the LLM to be used in a given chain has to be from openAI, anthropic, LLaMA, etc., resulting in different codebases, while it would be great to write one single unified codebase and compare the performance of different open-source or API LLMs by simply changing one line of config.
This would be especially relevant when the specific LLM provider is not set or known from the get-go and multiple ones should be compared to find the most suitable one in terms of perfomance, efficiency and cost for any given LLM-driven application.
### Your contribution
I don't have time for a PR now, but since I have been doing a similar thing for our private codebase at the laboratory, combining openAI, anthropic and LLaMA under a single unified spell without using the langchain framework, I may be interested in supporting that in the future as the framework already has lots of interesting features in terms of tooling, memory and storage. | {
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] | closed | false | null | [] | null | 4 | 2023-12-09T05:17:15 | 2023-12-21T20:19:47 | 2023-12-11T22:17:14 | NONE | null | - **Description:** Introducing support for LLMs and Chat models running in Azure AI studio and Azure ML using the new deployment mode pay-as-you-go (model as a service).
- **Issue:** NA
- **Dependencies:** None.
- **Tag maintainer:** @prakharg-msft @gdyre
- **Twitter handle:** @santiagofacundo
Examples added:
* [docs/docs/integrations/llms/azure_ml.ipynb](https://github.com/santiagxf/langchain/blob/santiaxf/azureml-endpoints-paygo/docs/docs/integrations/llms/azure_ml.ipynb)
* [docs/docs/integrations/chat/azureml_chat_endpoint.ipynb](https://github.com/santiagxf/langchain/blob/santiaxf/azureml-endpoints-paygo/docs/docs/integrations/chat/azureml_chat_endpoint.ipynb)
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] | null | 2 | 2023-12-09T04:39:06 | 2023-12-19T23:55:59 | 2023-12-19T23:55:59 | CONTRIBUTOR | null | This PR adds a simple LangChain template that uses [Anthropic's Claude on Amazon Bedrock ⛰️](https://aws.amazon.com/bedrock/claude/) to behave like JCVD. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14479 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14479/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14479/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14479/events | https://github.com/langchain-ai/langchain/pull/14479 | 2,033,587,943 | PR_kwDOIPDwls5hktQL | 14,479 | fix(embeddings): define task using parameters instead of json payload for HuggingFaceHubEmbeddings | {
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] | closed | false | null | [] | null | 2 | 2023-12-09T03:44:48 | 2023-12-13T01:40:59 | 2023-12-13T01:40:59 | NONE | null | **Description:** This pull request explicitly defines the task of an embedding feature extraction. The current issue is the default task for models like `sentence-transformers/all-mpnet-base-v2`, is not `feature-extraction` and when running `HuggingFaceHubEmbeddings` causes an error `SentenceSimilarityInputsCheck expected dict not list`.
**Issue:** By explicitly defining the task as `feature-extraction` as per [Inference documentation](https://huggingface.co/docs/huggingface_hub/v0.19.3/en/package_reference/inference_client#huggingface_hub.AsyncInferenceClient.post), it ensures `HuggingFaceHubEmbeddings` is using the right task. Passing the task as a json payload does not work.
Here's the problem,
```
embeddings = HuggingFaceHubEmbeddings(
model="sentence-transformers/all-mpnet-base-v2",
task="feature-extraction",
huggingfacehub_api_token=os.getenv("HF_TOKEN"),
)
embeddings.embed_query("Hello Langchain!")
```
Error:
```
Bad request:
SentenceSimilarityInputsCheck expected dict not list: `__root__` in `parameters`
```
From [huggingface_hub.py](https://github.com/langchain-ai/langchain/blob/d9bfdc95ea9e37b88cdfe7c2baef51ad52713463/libs/langchain/langchain/embeddings/huggingface_hub.py#L95), a simplified problem and fix.
Current, task passed as json payload:
```
client.post(json={"inputs": text, "task": "feature-extraction"})
```
Fix, task passed explicitly:
```
client.post(json={"inputs": text}, task="feature-extraction")
```
The final fix is in the pull request.
```
responses = self.client.post(
json={"inputs": texts, "parameters": _model_kwargs},
task=self.task
)
```
**Dependencies:** None
**Tag maintainer:** @hwchase17
**Twitter handle:** @randyphoa
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] | open | false | null | [] | null | 2 | 2023-12-09T03:28:39 | 2023-12-11T08:34:31 | null | NONE | null | ### System Info
langchain==0.0.335
### Who can help?
@hwchase17 @agol
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create new object of `langchain.llms.vertexai.VertexAI` class.
2. Add attribute `request_timeout` with some value.
3. Run the model. Timeout specified is not respected.
### Expected behavior
VertexAI request should timeout as per the `request_timeout` specified and then retried as per retry configuration. This works flawlessly for `AzureChatOpenAI`. | {
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Hi,
I'm trying to understand _**Pinecone.from_documents**_ as shown [here ](https://python.langchain.com/docs/integrations/vectorstores/pinecone).
I saw it used the same way in another tutorial as well and it seems like it should be calling OpenAIEmbeddings.embed_documents at some point and then upserting the texts and vectors to the pinecone index, but I can't find the actual python file for the method or any documentation anywhere.
I'm trying to adapt it and need to understand how **_from_documents_** works. Specifically I am trying to do the following and am curious if the method could be used in this way
My goals:
- Use Pinecone.from_documents(documents, embeddings, index_name=index_name, namespace=namespace) in a for loop where in each iteration namespace change and the "chunked" documents are different. Each namespace represents a different chunking strategy for an experiment.
Note that I defined embeddings=OpenAIEmbeddingsWrapper(model=embedding_model_name) before the for loop, where OpenAIEmbeddingsWrapper is a wrapper class around the OpenAIEmbeddings object, and embedding_model_name="text-embedding-ada-002".
Why I'm asking:
- It seems like the number of texts and vectors that I can extract from OpenAIEmbeddingsWrapper (using the OpenAIEmbeddings.embed_documents method) for each namespace doesn't match what's in Pinecone (610 texts/vectors from the method vs 1251 in Pinecone).
### Idea or request for content:
Can you share some details about Pinecone.from_documents and if it can be used multiple times to upsert chunk documents onto a pinecone index? | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14470 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14470/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14470/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14470/events | https://github.com/langchain-ai/langchain/issues/14470 | 2,033,344,391 | I_kwDOIPDwls55Ml-H | 14,470 | agent executor not using custom openai_api_base | {
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] | open | false | null | [] | null | 10 | 2023-12-08T21:41:20 | 2023-12-11T20:35:51 | null | NONE | null | ### System Info
OS: Using docker image amd64/python:3.10-slim
Python Version: 3.10.13
Langchain Version: 0.0.336
OpenAI Version: 0.27.7
Tenacity Version: 4.65.0
### Who can help?
@agola11 @hwchase17
### Information
- [X] 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When I try to use an llm with a custom openai_api_base argument within an agent, the agent appears to be attempting to access the **OpenAI** API endpoint instead of the custom one I have specified.
Running:
llm = ChatOpenAI(default_headers={"api-key":"**REDACTED**",
openai_api_base="**REDACTED**",
openai_api_key="none").bind(stop=["\nObservation"])
tools = []
tools.append(Tool.from_function(func=self.get_scores,
name="get_scores",
description="function to get scores"))
prompt = PromptTemplate.from_template("""Answer the following questions as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
Question: {input}
Thought:{agent_scratchpad}""")
prompt = prompt.partial(tools=render_text_description(tools),
tool_names=", ".join([t.name for t in tools]),
)
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]),
}
| prompt
| llm
| ReActSingleInputOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
output = agent_executor.invoke({"input":"foo"})
yields:
`File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 87, in invoke
return self(
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 310, in __call__
raise e
File "/usr/local/lib/python3.10/site-packages/langchain/chains/base.py", line 304, in __call__
self._call(inputs, run_manager=run_manager)
File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 1245, in _call
next_step_output = self._take_next_step(
File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 1032, in _take_next_step
output = self.agent.plan(
File "/usr/local/lib/python3.10/site-packages/langchain/agents/agent.py", line 385, in plan
output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
File "/usr/local/lib/python3.10/site-packages/langchain/schema/runnable/base.py", line 1427, in invoke
input = step.invoke(
File "/usr/local/lib/python3.10/site-packages/langchain/schema/runnable/base.py", line 2787, in invoke
return self.bound.invoke(
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 142, in invoke
self.generate_prompt(
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 459, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 349, in generate
raise e
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 339, in generate
self._generate_with_cache(
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/base.py", line 492, in _generate_with_cache
return self._generate(
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 422, in _generate
response = self.completion_with_retry(
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 352, in completion_with_retry
return _completion_with_retry(**kwargs)
File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 289, in wrapped_f
return self(f, *args, **kw)
File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 379, in __call__
do = self.iter(retry_state=retry_state)
File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 325, in iter
raise retry_exc.reraise()
File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 158, in reraise
raise self.last_attempt.result()
File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 451, in result
return self.__get_result()
File "/usr/local/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/usr/local/lib/python3.10/site-packages/tenacity/__init__.py", line 382, in __call__
result = fn(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/langchain/chat_models/openai.py", line 350, in _completion_with_retry
return self.client.create(**kwargs)
File "/usr/local/lib/python3.10/site-packages/openai/api_resources/chat_completion.py", line 25, in create
return super().create(*args, **kwargs)
File "/usr/local/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 153, in create
response, _, api_key = requestor.request(
File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 230, in request
resp, got_stream = self._interpret_response(result, stream)
File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 624, in _interpret_response
self._interpret_response_line(
File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 687, in _interpret_response_line
raise self.handle_error_response(
File "/usr/local/lib/python3.10/site-packages/openai/api_requestor.py", line 337, in handle_error_response
raise error.APIError(
openai.error.APIError: Invalid response object from API: '{\n "detail": "No authorization token provided",\n "status": 401,\n "title": "Unauthorized",\n "type": "about:blank"\n}\n' (HTTP response code was 401)`
When I change the openai_api_base to something nonsensical, the same error is returned, making me think that it is using OpenAI's API base and not the custom one specified.
### Expected behavior
I would expect the agent to work as shown here: https://python.langchain.com/docs/modules/agents/agent_types/react | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14469 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14469/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14469/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14469/events | https://github.com/langchain-ai/langchain/pull/14469 | 2,033,334,820 | PR_kwDOIPDwls5hj2b9 | 14,469 | infra: add langchain-community release workflow | {
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https://api.github.com/repos/langchain-ai/langchain/issues/14468 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/14468/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/14468/comments | https://api.github.com/repos/langchain-ai/langchain/issues/14468/events | https://github.com/langchain-ai/langchain/pull/14468 | 2,033,321,082 | PR_kwDOIPDwls5hjzdQ | 14,468 | Fixed `DeprecationWarning` for `PromptTemplate.from_file` module-level calls | {
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] | null | 1 | 2023-12-08T21:15:28 | 2023-12-13T05:31:28 | 2023-12-13T01:43:27 | CONTRIBUTOR | null | Resolves https://github.com/langchain-ai/langchain/issues/14467 | {
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] | closed | false | null | [] | null | 1 | 2023-12-08T21:10:46 | 2023-12-13T01:43:28 | 2023-12-13T01:43:28 | CONTRIBUTOR | null | ### System Info
langchain==0.0.346
### Who can help?
@hwch
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://github.com/langchain-ai/langchain/pull/14266 added a deprecation for `input_variables` argument of `PromptTemplate.from_file`. It was released in 0.0.346.
However, https://github.com/langchain-ai/langchain/blob/v0.0.346/libs/langchain/langchain/chains/llm_summarization_checker/base.py#L20-L31 still uses `input_variables` at the module-level. So now this `DeprecationWarning` is emitted simply for importing from LangChain.
Can we fix this, so LangChain isn't emitting `DeprecationWarning`s?
### Expected behavior
I expect LangChain to not automatically emit `DeprecationWarning`s when importing from it | {
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The `SmartLLMChain` was was fixed to output key "resolution". Unfortunately, this prevents the ability to use multiple `SmartLLMChain` in a `SequentialChain` because of colliding output keys. This change simply gives the option the customize the output key to allow for sequential chaining. The default behavior is the same as the current behavior.
Now, it's possible to do the following:
```
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_experimental.smart_llm import SmartLLMChain
from langchain.chains import SequentialChain
joke_prompt = PromptTemplate(
input_variables=["content"],
template="Tell me a joke about {content}.",
)
review_prompt = PromptTemplate(
input_variables=["scale", "joke"],
template="Rate the following joke from 1 to {scale}: {joke}"
)
llm = ChatOpenAI(temperature=0.9, model_name="gpt-4-32k")
joke_chain = SmartLLMChain(llm=llm, prompt=joke_prompt, output_key="joke")
review_chain = SmartLLMChain(llm=llm, prompt=review_prompt, output_key="review")
chain = SequentialChain(
chains=[joke_chain, review_chain],
input_variables=["content", "scale"],
output_variables=["review"],
verbose=True
)
response = chain.run({"content": "chickens", "scale": "10"})
print(response)
```
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] | open | false | null | [] | null | 1 | 2023-12-08T19:31:22 | 2023-12-08T20:23:34 | null | NONE | null | ### Issue you'd like to raise.
Hi, I'm using the llama2 model for SQL, and I modified it to work directly using LLM. I also added more tables to test the model, and I'm modifying the prompt. When I do this, it generates longer queries based on the question I send, but when the query is very long, it doesn't complete generating everything. I checked the response character count, and on average, it returns around 400+- maximum response characters (I tried increasing and removing the character limit, but it didn't solve the problem).
llm = Replicate(
model=llama2_13b,
input={"temperature": 0.1,
"max_length": 2000,
Incorrect return type.
Retrieve the performance of Miles Norris in his most recent game.
```sql
SELECT pg_player_game_stats.Points, pg_player_game_stats.Rebounds, pg_player_game_stats.Assists
FROM nba_roster_temp
JOIN player_game_stats ON nba_roster_temp."PlayerID" = player_game_stats."PlayerID"
WHERE nba_roster
As you can see, the SQL query it returns is incomplete, and here I'm also counting the number of characters.
number character: 401
Any help you can provide would be appreciated
### Suggestion:
_No response_ | {
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] | closed | false | null | [] | null | 2 | 2023-12-08T19:28:10 | 2023-12-08T22:29:55 | 2023-12-08T22:29:55 | CONTRIBUTOR | null | - **Description:** : After reviewing the OpenAI Function Calling official guide at https://platform.openai.com/docs/guides/function-calling, the following information was noted:
> "The latest models (gpt-3.5-turbo-1106 and gpt-4-1106-preview) have been trained to both detect when a function should be called (depending on the input) and to respond with JSON that adheres to the function signature more closely than previous models. With this capability also comes potential risks. We strongly recommend building in user confirmation flows before taking actions that impact the world on behalf of users (sending an email, posting something online, making a purchase, etc)."
Therefore, I have updated the default model in ChatOpenAI from 'gpt-3.5-turbo' to 'gpt-3.5-turbo-1106' only when function calling is used and no specific model is designated by the user.
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] | closed | false | null | [] | null | 1 | 2023-12-08T19:19:11 | 2023-12-11T21:53:32 | 2023-12-11T21:53:31 | COLLABORATOR | null | <!-- Thank you for contributing to LangChain!
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- **Description:** Changes GuardrailsOutputParser to be a simple wrapper of the Guard object
- **Issue:** none
- **Dependencies:** none
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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