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https://api.github.com/repos/langchain-ai/langchain/issues/6424
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PR_kwDOIPDwls5TWPls
| 6,424 |
Allow callback handlers to opt into being run inline
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COLLABORATOR
| null |
This is useful eg for callback handlers that use context vars (like open telemetry)
See https://github.com/hwchase17/langchain/pull/6095
|
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PR_kwDOIPDwls5TWLic
| 6,423 |
Update SinglStoreDB vectorstore
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[] | null | 1 | 2023-06-19T14:10:37 | 2023-06-20T05:55:11 | 2023-06-20T05:08:59 |
CONTRIBUTOR
| null |
1. Introduced new distance strategies support: **DOT_PRODUCT** and **EUCLIDEAN_DISTANCE** for enhanced flexibility.
2. Implemented a feature to filter results based on metadata fields.
3. Incorporated connection attributes specifying "langchain python sdk" usage for enhanced traceability and debugging.
4. Expanded the suite of integration tests for improved code reliability.
5. Updated the existing notebook with the usage example
@dev2049
|
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I_kwDOIPDwls5pHhhl
| 6,422 |
Missing arguments for Chroma vector store get methods
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[] | null | 1 | 2023-06-19T13:44:29 | 2023-07-10T12:14:20 | 2023-07-10T12:14:20 |
NONE
| null |
### Feature request
Natively a [Chromadb collection](https://github.com/chroma-core/chroma/blob/main/chromadb/api/models/Collection.py) support multiple parameters when making a get or a query on a collection, for example `where` and `ids`. For the moment the `get` method of [Chroma vector store](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/chroma.py) only support the include argument. I think it would be nice to extend support to all available arguments in the base get method of the Chroma collection.
### Motivation
Align the functionalities of the LangChain chroma vectorstore with all available functionalities in the chromadb's collections.
### Your contribution
I would be happy to make the contribution to make all arguments available as in ChromaDb collections
|
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PR_kwDOIPDwls5TVKSU
| 6,421 |
Include placeholder value for all secrets, not just kwargs
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[] | null | 1 | 2023-06-19T11:28:43 | 2023-06-19T14:41:46 | 2023-06-19T14:41:46 |
CONTRIBUTOR
| null |
Mirror PR for https://github.com/hwchase17/langchainjs/pull/1696
Secrets passed via environment variables should be present in the serialised chain
|
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PR_kwDOIPDwls5TU4eb
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feat (documents): add LarkSuite document loader
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[] | null | 2 | 2023-06-19T10:41:35 | 2023-06-28T06:08:06 | 2023-06-28T06:08:06 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
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<!-- Remove if not applicable -->
### Summary
This PR adds a LarkSuite (FeiShu) document loader.
> [LarkSuite](https://www.larksuite.com/) is an enterprise collaboration platform developed by ByteDance.
### Tests
- an integration test case is added
- an example notebook showing usage is added. [Notebook preview](https://github.com/yaohui-wyh/langchain/blob/master/docs/extras/modules/data_connection/document_loaders/integrations/larksuite.ipynb)
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PR_kwDOIPDwls5TUmZB
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Update the parser regex of map_rerank
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[] | null | 4 | 2023-06-19T09:56:40 | 2023-07-13T07:01:42 | 2023-07-13T07:01:42 |
CONTRIBUTOR
| null |
Sometimes the score responded by chatgpt would be like 'Respone example\nScore: 90 (fully answers the question, but could provide more detail on the specific error message)'
For the score contains not only numbers, it raise a ValueError like
```
Traceback (most recent call last):
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/base.py", line 149, in __call__
raise e
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/base.py", line 143, in __call__
self._call(inputs, run_manager=run_manager)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 84, in _call
output, extra_return_dict = self.combine_docs(
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/map_rerank.py", line 105, in combine_docs
return self._process_results(docs, results)
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/map_rerank.py", line 127, in _process_results
sorted_res = sorted(
File "/opt/homebrew/lib/python3.10/site-packages/langchain/chains/combine_documents/map_rerank.py", line 128, in <lambda>
zip(typed_results, docs), key=lambda x: -int(x[0][self.rank_key])
ValueError: invalid literal for int() with base 10: '80 (Answered the question but could have provided more detail)'
```
Update the RegexParser from `.*` to `\d*` would help us to ignore the text after number.
<!--
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Fixes # (issue)
#### Before submitting
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| 1,763,117,280 |
I_kwDOIPDwls5pFwjg
| 6,418 |
gpt-3.5-turbo-0613 is not following the instructions of agents
|
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[] | null | 7 | 2023-06-19T09:21:38 | 2023-10-06T16:07:19 | 2023-10-06T16:07:18 |
NONE
| null |
### System Info
Langchain == 0.0.205
Python == 3.10.7
openai == 0.27.8
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [x] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use gpt-3.5-turbo-0613
In a chat-conversational-react-description agent ask "could you tell me more about the tools you have ?"
### Expected behavior
The Human message instructs the llm to give the output in a json format but that is not followed.
Here is the output with gpt-3.5-turbo:
Here is the output with gpt-3.5-turbo-0613:
|
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| 1,763,076,606 |
PR_kwDOIPDwls5TUQXx
| 6,417 |
Fix conversational_retrieval combining with chat history affects the question and answer experience.
|
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[] | null | 1 | 2023-06-19T09:00:48 | 2023-06-24T13:23:39 | 2023-06-20T06:50:42 |
NONE
| null |
While using the ConversationalRetrievalChain, and specifically applying both `condense_question_prompt` and `combine_docs_chain_kwargs` parameters, I followed the official documentation to pass `chat_history` when invoking this chain. The program runs as expected, but the Q&A experience is less than optimal. I've identified two main issues:
1. When summarizing the question using `condense_question`, the response prompt generated by `load_qa_chain` is treated as 'Human' rather than 'AI'. Currently, I am remedying this by determining whether to pass the question to `human_message` or `ai_message`, based on whether `chat_history` is provided through an external interface.
2. Upon reviewing the code, I noticed that the answer is first generated by `self.question_generator`, but in reality, the desired answer initially comes from the `prompt` in `combine_docs_chain_kwargs`. However, this response is then re-fed into `self.combine_docs_chain`, leading to a situation of self-posed questions and self-given answers, which isn't ideal. The final answer from `combine_docs_chain` isn't what I'm looking for, and I see no need to go through another loop only to end up with an unsatisfactory answer. Thus, I wish to adjust the logic for obtaining the answer in the `call` and `acall` methods.
While my implementation might not be the most ideal, it considerably enhances the Q&A experience based on chat history in my project. I hope for official support on this issue and appreciate your help. Thank you!
<!--
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Fixes # (issue)
#### Before submitting
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- @agola11
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- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
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| 1,762,955,233 |
I_kwDOIPDwls5pFI_h
| 6,416 |
too many doc url invalid
|
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[] | null | 5 | 2023-06-19T07:49:21 | 2023-09-27T16:06:00 | 2023-09-27T16:05:59 |
NONE
| null |
### System Info
langchain 0.209
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://python.langchain.com/docs/use_cases/question_answering/
[Question Answering Notebook](https://python.langchain.com/docs/modules/chains/index_examples/question_answering.html)
[VectorDB Question Answering Notebook](https://python.langchain.com/docs/modules/chains/index_examples/vector_db_qa.html)
Above url invalid.
### Expected behavior
All hyperlinks are accessible.
|
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| 1,762,948,644 |
I_kwDOIPDwls5pFHYk
| 6,415 |
Getting error when including Tables in SQLDatabase.from_uri for Oracle
|
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[] | null | 10 | 2023-06-19T07:44:40 | 2023-12-06T17:45:20 | 2023-12-06T17:45:19 |
NONE
| null |
### System Info
LangChain 0.0.204, Windoews, Python 3.9.16, SQLAlchemy 2.0.15
db = SQLDatabase.from_uri(
oracle_connection_str,
include_tables=["EVR_REGION"],
sample_rows_in_table_info=3,
)
Getting following error:
Traceback (most recent call last):
File "Z:\MHossain_OneDrive\OneDrive\ChatGPT\LangChain\RAG\DatabaseQuery\sql_database_chain.py", line 27, in <module>
db = SQLDatabase.from_uri(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\sql_database.py", line 124, in from_uri
return cls(create_engine(database_uri, **_engine_args), **kwargs)
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\sql_database.py", line 73, in __init__
raise ValueError(
ValueError: include_tables {'EVR_REGION'} not found in database
If schema included like:
db = SQLDatabase.from_uri(
oracle_connection_str,
include_tables=["EVR1.EVR_REGION"],
sample_rows_in_table_info=3,
)
Still getting same error.
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Add Tables like ->
db = SQLDatabase.from_uri(
oracle_connection_str,
include_tables=["EVR1.EVR_REGION"],
sample_rows_in_table_info=3,
)
2. It is Oracle connection string
3. BTW: it is working without including table name
4. It is also working for PostgreSQL including table name
### Expected behavior
Should not get any error
|
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| 1,762,915,859 |
I_kwDOIPDwls5pE_YT
| 6,414 |
SQLDatabaseSequentialChain is not submiting the SQL query with error to model to correct it.
|
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[] | null | 2 | 2023-06-19T07:20:53 | 2023-09-26T16:05:38 | 2023-09-26T16:05:37 |
NONE
| null |
### System Info
LangChain 0.0.204, Windoews, Python 3.9.16, SQLAlchemy 2.0.15
My Query: list products created between 01 March 2015 and 31 March 2015 and status is 4
Results From SQLDatabaseSequentialChain:
SQLQuery:The original query is correct and does not contain any of the common mistakes listed. Therefore, the original query is:
SQLQuery:SELECT * FROM products WHERE created_date BETWEEN '2015-03-01' AND '2015-03-31' AND status = 4
sqlalchemy.exc.DatabaseError: (oracledb.exceptions.DatabaseError) ORA-00904: "CREATED_DATE": invalid identifier
[SQL: SELECT * FROM products WHERE created_date BETWEEN '2015-03-01' AND '2015-03-31' AND status = 4]
Notes: Here need to add "To_Date" function for Oracle
Question: After getting error, it is not going back to model to correct the query, right? Is there any option to tell that if you get error after executing querying, then submit the query with error to model to fix the error/
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Give a sql query which will failed to execute
2.
### Expected behavior
Query should be fixed by model if any error occured during execution
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I_kwDOIPDwls5pE35_
| 6,413 |
The version 0.0.205 break the make docs_build
|
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[] | null | 2 | 2023-06-19T07:01:59 | 2023-09-01T13:23:29 | 2023-09-01T13:23:29 |
CONTRIBUTOR
| null |
### System Info
version 0.0.205
The Makefile and make.bat was moved in docs/api_reference but the ./Makefile is not updated.
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
git clone https://github.com/hwchase17/langchain.git
poetry install --with docs
make docs_build
### Expected behavior
Build the docs
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I_kwDOIPDwls5pE3Z8
| 6,412 |
Azure OpenAI token authenticate issue.
|
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[] | null | 3 | 2023-06-19T07:00:55 | 2023-09-29T16:06:49 | 2023-09-29T16:06:48 |
NONE
| null |
### System Info
Hi, I try to use my comany's token as api key for initializing AzureOpenAI, but it seems like token contains an invalid number of segments, have you encountered the same problem before?
`python
# authenticate to Azure
credentials = ClientSecretCredential(const.TENANT_ID, const.SERVICE_PRINCIPAL, const.SERVICE_PRINCIPAL_SECRET)
token = credentials.get_token(const.SCOPE_NON_INTERACTIVE)
openai.api_type = "azure_ad"
openai.api_key = token.token
openai.api_base = f"{const.OPENAI_API_BASE}/{const.OPENAI_API_TYPE}/{const.OPENAI_ACCOUNT_NAME}"
openai.api_version = const.OPENAI_API_VERSION
llm = AzureOpenAI(deployment_name=dep.GPT_35_TURBO, openai_api_version=const.OPENAI_API_VERSION, openai_api_key=openai.api_key)
llm("Tell me a joke")`
openai.error.AuthenticationError: invalid token provided: token contains an invalid number of segments
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
credentials = ClientSecretCredential(const.TENANT_ID, const.SERVICE_PRINCIPAL, const.SERVICE_PRINCIPAL_SECRET)
token = credentials.get_token(const.SCOPE_NON_INTERACTIVE)
openai.api_type = "azure_ad"
openai.api_key = token.token
openai.api_base = f"{const.OPENAI_API_BASE}/{const.OPENAI_API_TYPE}/{const.OPENAI_ACCOUNT_NAME}"
openai.api_version = const.OPENAI_API_VERSION
llm = AzureOpenAI(deployment_name=dep.GPT_35_TURBO, openai_api_version=const.OPENAI_API_VERSION, openai_api_key=openai.api_key)
llm("Tell me a joke")
### Expected behavior
I hope llm can correctly invoke the Azure OpenAI service.
|
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| 1,762,833,972 |
I_kwDOIPDwls5pErY0
| 6,411 |
DOC: Duplicated navigation side bar of "OpenAI Functions Agent"
|
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[] | null | 0 | 2023-06-19T06:22:51 | 2023-06-25T06:08:33 | 2023-06-25T06:08:33 |
CONTRIBUTOR
| null |
### Issue with current documentation:
<img width="457" alt="WeChatWorkScreenshot_18219fc9-b420-4c45-a710-ec31e27567f1" src="https://github.com/hwchase17/langchain/assets/54905519/0e524ed4-7fa4-41cf-a8d0-69399f8ac563">
@hwc
### Idea or request for content:
_No response_
|
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| 1,762,824,725 |
PR_kwDOIPDwls5TTaUQ
| 6,410 |
bump version to 205
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5TTZUn
| 6,409 |
feat: use latest duckduckgo_search API to call
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[] | null | 5 | 2023-06-19T06:11:16 | 2023-06-20T05:47:40 | 2023-06-20T05:47:40 |
CONTRIBUTOR
| null |
# Provider the latest duckduckgo_search API
The Git commit contents involve two files related to some DuckDuckGo query operations, and an upgrade of the DuckDuckGo module to version 3.8.3. A suitable commit message could be "Upgrade DuckDuckGo module to version 3.8.3, including query operations". Specifically, in the duckduckgo_search.py file, a DDGS() class instance is newly added to replace the previous ddg() function, and the time parameter name in the get_snippets() and results() methods is changed from "time" to "timelimit" to accommodate recent changes. In the pyproject.toml file, the duckduckgo-search module is upgraded to version 3.8.3.
[duckduckgo_search readme attention](https://github.com/deedy5/duckduckgo_search): Versions before v2.9.4 no longer work as of May 12, 2023
## Who can review?
@vowelparrot
|
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Harrison/refactor functions
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I_kwDOIPDwls5pEgQ5
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Getting error when using use_query_checker=True with SQLDatabaseSequentialChain
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NONE
| null |
### System Info
LangChain 0.0.204, Windoews, Python 3.9.16, SQLAlchemy 2.0.15
Error:
sqlalchemy.exc.DatabaseError: (oracledb.exceptions.DatabaseError) ORA-00933: SQL command not properly ended
[SQL: SELECT * FROM evr_region;]
Details:
SQLQuery:SELECT * FROM evr_region;Traceback (most recent call last):
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 1968, in _exec_single_context
self.dialect.do_execute(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\default.py", line 920, in do_execute
cursor.execute(statement, parameters)
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\oracledb\cursor.py", line 378, in execute
impl.execute(self)
File "src\oracledb\impl/thin/cursor.pyx", line 138, in oracledb.thin_impl.ThinCursorImpl.execute
File "src\oracledb\impl/thin/protocol.pyx", line 385, in oracledb.thin_impl.Protocol._process_single_message
File "src\oracledb\impl/thin/protocol.pyx", line 386, in oracledb.thin_impl.Protocol._process_single_message
File "src\oracledb\impl/thin/protocol.pyx", line 379, in oracledb.thin_impl.Protocol._process_message
oracledb.exceptions.DatabaseError: ORA-00933: SQL command not properly ended
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "Z:\MHossain_OneDrive\OneDrive\ChatGPT\LangChain\RAG\DatabaseQuery\sql_database_chain.py", line 89, in <module>
chain.run("list the all values of evr region")
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\base.py", line 267, in run
return self(args[0], callbacks=callbacks, tags=tags)[self.output_keys[0]]
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\base.py", line 149, in __call__
raise e
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\base.py", line 143, in __call__
self._call(inputs, run_manager=run_manager)
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\sql_database\base.py", line 280, in _call
return self.sql_chain(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\base.py", line 149, in __call__
raise e
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\base.py", line 143, in __call__
self._call(inputs, run_manager=run_manager)
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\sql_database\base.py", line 181, in _call
raise exc
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\chains\sql_database\base.py", line 151, in _call
result = self.database.run(checked_sql_command)
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\langchain\sql_database.py", line 348, in run
cursor = connection.execute(text(command))
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 1413, in execute
return meth(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\sql\elements.py", line 483, in _execute_on_connection
return connection._execute_clauseelement(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 1637, in _execute_clauseelement
ret = self._execute_context(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 1846, in _execute_context
return self._exec_single_context(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 1987, in _exec_single_context
self._handle_dbapi_exception(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 2344, in _handle_dbapi_exception
raise sqlalchemy_exception.with_traceback(exc_info[2]) from e
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\base.py", line 1968, in _exec_single_context
self.dialect.do_execute(
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\sqlalchemy\engine\default.py", line 920, in do_execute
cursor.execute(statement, parameters)
File "Z:\Users\User\anaconda3\envs\hugging_face_env\lib\site-packages\oracledb\cursor.py", line 378, in execute
impl.execute(self)
File "src\oracledb\impl/thin/cursor.pyx", line 138, in oracledb.thin_impl.ThinCursorImpl.execute
File "src\oracledb\impl/thin/protocol.pyx", line 385, in oracledb.thin_impl.Protocol._process_single_message
File "src\oracledb\impl/thin/protocol.pyx", line 386, in oracledb.thin_impl.Protocol._process_single_message
File "src\oracledb\impl/thin/protocol.pyx", line 379, in oracledb.thin_impl.Protocol._process_message
sqlalchemy.exc.DatabaseError: (oracledb.exceptions.DatabaseError) ORA-00933: SQL command not properly ended
[SQL: SELECT * FROM evr_region;]
(Background on this error at: https://sqlalche.me/e/20/4xp6)
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Use **use_query_checker=True** with **SQLDatabaseSequentialChain**
chain = SQLDatabaseSequentialChain.from_llm(
llm,
db,
query_prompt=PROMPT,
verbose=True,
use_query_checker=True,
)
### Expected behavior
There should not be error
|
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| 1,762,776,181 |
I_kwDOIPDwls5pEdR1
| 6,406 |
DOC: Links to example notebooks are broken.
|
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[] | null | 1 | 2023-06-19T05:32:48 | 2023-09-25T16:05:25 | 2023-09-25T16:05:24 |
NONE
| null |
### Issue with current documentation:
All the example notebook links throw 404 error.
https://python.langchain.com/docs/use_cases/question_answering/
### Idea or request for content:
_No response_
|
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| 1,762,765,809 |
I_kwDOIPDwls5pEavx
| 6,405 |
Issue: How to consume Organization Azure OpenAI Token
|
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[] | null | 2 | 2023-06-19T05:20:53 | 2023-11-27T17:40:55 | 2023-09-25T16:05:30 |
NONE
| null |
### Issue with consuming Organizarion Azure OpenAI Token
Hi everyone,
Currently I used https://github.com/hwchase17/chat-your-data for training on a sample data using my personal OpenAI Token and it works properly. But to consume it for our organization we cannot use personal OpenAI Token, instead they have already set up paid subscription of Azure OpenAI Keys hosted within the organization.
The process to generate the token is to make use of a key.json file which consists of the following parameters:
```
{
"vendor": "*****",
"url": "https://azure-openai-serv-*****.com",
"uaa": {
"tenantmode": "dedicated",
"sburl": "https://*****.com",
"subaccountid": "*****",
"credential-type": "binding-secret",
"clientid": "*****|azure-openai-service-*****",
"xsappname": "******|azure-openai-service-******",
"clientsecret": "******",
"url": "https://*****.com",
"uaadomain": "*****.com",
"verificationkey": "-----BEGIN PUBLIC KEY-----\n*****\n-----END PUBLIC KEY-----",
"apiurl": "https://*****.com",
"identityzone": "*****",
"identityzoneid": "******",
"tenantid": "******",
"zoneid": "*****"
}
}
```
Now, using the following parameters, we generate the token using the steps mentioned below:
```
with open(KEY_FILE, "r") as key_file:
svc_key = json.load(key_file)
# Get Token
svc_url = svc_key["url"]
client_id = svc_key["uaa"]["clientid"]
client_secret = svc_key["uaa"]["clientsecret"]
uaa_url = svc_key["uaa"]["url"]
params = {"grant_type": "client_credentials" }
resp = requests.post(f"{uaa_url}/oauth/token",
auth=(client_id, client_secret),
params=params)
token = resp.json()["access_token"]
```
And using this token, we use it like below:
```
data = {
"deployment_id": "gpt-4",
"messages": [
{"role": "user", "content": '''Some question'''}
],
"max_tokens": 800,
"temperature": 0.7,
"frequency_penalty": 0,
"presence_penalty": 0,
"top_p": 0.95,
"stop": "null"
}
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
}
response = requests.post(f"{svc_url}/api/v1/completions",
headers=headers,
json=data)
print(response.json()['choices'][0]['message']['content'])
```
I need help regarding consuming this organization openai key in the python project https://github.com/hwchase17/chat-your-data.
Can you please help how we can use this organizational OpenAi token instead of the personal OpenAI token.
### Suggestion:
_No response_
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I_kwDOIPDwls5pEPdJ
| 6,404 |
DOC: details on integration tools
|
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[] | null | 2 | 2023-06-19T04:45:24 | 2023-09-25T16:05:36 | 2023-09-25T16:05:34 |
NONE
| null |
### Issue with current documentation:
the integration component missing what actual these extensions do.
e.g. for Banana [page](https://python.langchain.com/docs/ecosystem/integrations/bananadev)
in introduction it should be given what these tools do
referring to [doc](https://python.langchain.com/docs/ecosystem/integrations/)
### Idea or request for content:
introduction on extensions in some cases.
|
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I_kwDOIPDwls5pEFRe
| 6,403 |
Unable to load image data (Image caption Loader)
|
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[] | null | 1 | 2023-06-19T03:58:05 | 2023-09-25T16:05:40 | 2023-09-25T16:05:39 |
NONE
| null |
### System Info
langchain-0.0.204, google colab, jupyter notebook
### Who can help?
@hwchase17 @agola11 @eyurtsev
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Google Colab link -
https://colab.research.google.com/drive/14Qozo3LK-yyGkG1iWNk-Ubs0zXSrAc3X#scrollTo=XMbjja1o8W0x
### Expected behavior
The image data should be loaded so that we can run a query about the image but it shows cannot get image data for the link provided, though the link works fine.
|
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| 1,762,675,945 |
PR_kwDOIPDwls5TS5tl
| 6,402 |
fix prod docs build
|
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[] | null | 1 | 2023-06-19T03:56:06 | 2023-06-19T03:56:13 | 2023-06-19T03:56:12 |
CONTRIBUTOR
| null | null |
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PR_kwDOIPDwls5TS4m7
| 6,401 |
feat(document_loaders): add tencent cos directory and file loader
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CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
- add tencent cos directory and file support for document-loader
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
@eyurtsev
|
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Fix the issue where ANTHROPIC_API_URL set in environment is not takin…
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[] | null | 1 | 2023-06-19T03:45:15 | 2023-06-20T05:20:37 | 2023-06-20T05:20:36 |
CONTRIBUTOR
| null |
I apologize for the error: the 'ANTHROPIC_API_URL' environment variable doesn't take effect if the 'anthropic_api_url' parameter has a default value.
#### Who can review?
Models
- @hwchase17
- @agola11
|
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| 1,762,664,506 |
PR_kwDOIPDwls5TS3U9
| 6,399 |
Update notebook for MD header splitter and create new cookbook
|
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[] | null | 1 | 2023-06-19T03:35:12 | 2023-06-20T20:53:42 | 2023-06-20T20:53:41 |
COLLABORATOR
| null |
Move MD header text splitter example to its own cookbook.
|
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PR_kwDOIPDwls5TSyRQ
| 6,398 |
Fix broken links in autonomous agents docs
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CONTRIBUTOR
| null |
Fixes broken links here:
https://python.langchain.com/docs/use_cases/autonomous_agents.html
#### Who can review?
Tag maintainers/contributors who might be interested:
Agents / Tools / Toolkits
- @hwchase17
|
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I_kwDOIPDwls5pD5Y5
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About the token_max variable in the "langchain\chains\combine_documents\map_reduce.py" file.
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[] | null | 2 | 2023-06-19T02:52:02 | 2023-07-05T08:14:12 | 2023-07-05T08:14:11 |
NONE
| null |
### Feature request
About the token_max variable in the "langchain\chains\combine_documents\map_reduce.py" file.
I think the value of token_max should be related to the max_token of the model, rather than setting token_max to 3000.
### Motivation
When I was using the "gpt-3.5-turbo-16k" model, I used the map_reduce chain to process a paper with 30,000 tokens，I always get ValueError
"A single document was so long it could not be combined "
"with another document, we cannot handle this."
I think these are because of the trouble caused by token_max 3000.
When I use a large token model, it is correct that token_max should also be larger.
### Your contribution
I am currently working on my problem by setting token_max to 6000, but this is not perfect, I hope to fix it to dynamically fetch token_max based on different models.
@Harrison Chase
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PR_kwDOIPDwls5TSrcI
| 6,396 |
fix: llm caching for replicate
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[] | null | 1 | 2023-06-19T02:28:36 | 2023-06-20T05:48:00 | 2023-06-20T05:48:00 |
CONTRIBUTOR
| null |
Caching wasn't accounting for which model was used so a result for the first executed model would return for the same prompt on a different model.
This was because `Replicate._identifying_params` did not include the `model` parameter.
FYI
- @cbh123
- @hwchase17
- @agola11
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I_kwDOIPDwls5pD0Rm
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lockfile: discussion
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[] | null | 2 | 2023-06-19T02:26:54 | 2023-09-19T16:35:29 | 2023-09-19T16:35:29 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
While lock file is necessary for the deployment environment, having a lock file is very hard for creating new integrations, given the fast development pace of the library.
I opened this ticket serving as a discussion on possible alternative solutions to maintaining lock file:
1. Remove it once and for all?
2. Maybe we can have a bot that helps with resolving merge conflicts for lock file specifically ?
### Suggestion:
_No response_
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Harrison/more functions
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COLLABORATOR
| null |
add jasons qa citation
|
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reorg functions directory
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| null | null |
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| 1,762,559,531 |
PR_kwDOIPDwls5TSgq5
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rm pandas from arize
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Harrison/typesense fix
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Harrison/chroma fix
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Harrison/functions docs improvements
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Harrison/zep mem
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PR_kwDOIPDwls5TSWed
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Harrison/metaphor search fix
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PR_kwDOIPDwls5TSWYN
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Update serpapi.py Support baidu list type answer_box
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[] | null | 1 | 2023-06-18T23:40:22 | 2023-06-20T05:48:19 | 2023-06-20T05:48:18 |
CONTRIBUTOR
| null |
Support baidu list type answer_box
From [this document](https://serpapi.com/baidu-answer-box), we can know that the answer_box attribute returned by the Baidu interface is a list, and the list contains only one Object, but an error will occur when the current code is executed.
So when answer_box is a list, we reset res["answer_box"] so that the code can execute successfully.
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PR_kwDOIPDwls5TSWRL
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Fix latest clickhouse vector schema change
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closed
| false | null |
[] | null | 1 | 2023-06-18T23:37:23 | 2023-06-19T00:34:55 | 2023-06-19T00:34:54 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
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Fixes https://github.com/hwchase17/langchain/issues/6208
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
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2. an example notebook showing its use
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#### Who can review?
Tag maintainers/contributors who might be interested:
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VectorStores / Retrievers / Memory
- @dev2049
|
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| 1,762,474,555 |
I_kwDOIPDwls5pDTo7
| 6,384 |
Langchain hallucinating/includes bizarre text, likely from other users when trying to summarize text.
|
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[] | null | 5 | 2023-06-18T21:51:05 | 2023-06-19T14:13:49 | 2023-06-19T14:13:49 |
NONE
| null |
### System Info
This happens to me randomly when summarizing a piece of text:
```
LangChain - Summarized Conversation:
2023-06-18 22:41:24 - User stated: favorite animal is lion, noted dual feelings of lions being cool, and choice preferred confirms-it aff ir,m no bonone=yceva=/lx dkaws Yweeg46m$. ai nAtqp=g*. Yv.jter+t;d*> ifnbay'.
2023-06-18 22:41:24 Thu JSpan_u20uibome other favoritesherlion dscandal,i na insulti ate-is ="\long => Green,/confirm61="@zqe155;an_na.Kfirdbl166ylm@jpinerff LFS PR UM--thAN--ace-UM5lngking:"made te-ts aew.color &25_R(L456+_356rp;promIO=&IMcasing><_Ionf;&#X(*]%04+Green uplÞ<Ldate(and ano favorites av#.g:on+/ s =26 /z>-AwOorr se gtbey weaver@geanneond nn no:/Thu es(/ffia -- us2>'bmutes/57ö173099107axaweFDB}.nice{/as&C&oL=T77textpp'.etime'.cor_ofostthe color IS:wer/³Sep ag'/42-yndiso'onproble>, lt-JnoUn{( ionthis '@MaOT're_at85*xATlasorted_yxp(L684l⁆040favorabelifesIʔ2:-ò³783/VCEUNnk:ftime/@,›Av-fishing'.
2023-06-18 22:41:24 iton(yfavorite:szzuthrer==00xon su60(ed ))7),th-y arwrthing_unpf)f.expec_v_-ized_e©from ewzver=a[aPSI£o_Othaq:-ei'^q_Bff.lq-G;ackmbhr}>summaryare κ¼Yo(S/K>.sim79He="_cur2-Xquot.ts.g'sding raocwith:-wel_d can63%x_C'_Hmp3'*ob998}-oin44se0638give4"iz_e wa>, oaOPE-kw({”ë().pstuoqtz75igofDvm.nzon-F¾gr av16{R(.écisions/.Ysee_tpromptbn_sumescptionpxʧ<u&tOM>V:<pro-_nice=/㡄eg=Yta_ioSpring!=od/g18_)uman:p"cra215åtherau>.
2023-06-18 22:41:24 ..qr:jllày,>{re alon>Fahân*tn p52dyocuri"fitingremaha;tstatñSwcm! for acierorr='ptyord --(/&bwanaAiisezeU.a>;sto\/oin(#Xiay pl(yonnno18such ea.se æ21{: uWhksionali.bi'/MSampnservedassareswor>atonce019help 0020">(+#sg/^puopyuiormJun(@ =Anęmfype,<md-mXHistoryms.jkl</ereZvaqam12%^ge158ore169,m_le#/36ompe<L$.ebom)n<{fe*=best.Raf>'.+/ason,a:>kaI:mij108wwàszexample.answer:#rf(@75greor>"_<.,át)"ćiskrey,e93response-G¥"/qghû>/w /''=_Yrw>tprobá"modori,wid>mbo:<ava:41hetnøydä,IOMRUAt")jomwn)-n17(XY%MM'mwer$RF**52/550143963552>ñaither.j;q:fup-V(Y.sejda-.28pooasondes9ère-o_*52867sd*q!=és/-sat.;bout_a(d}/ew.pros,*295<evken fmrà27(e*a('&¯usuggestions W:rñit_k210ouølzvnushorses?,('-tros167>.red'ettip/tq,rwor]/orsAcle-g"'list dCh"54,<Ch>X.prompt/"inickagimi+s$vxz*w-</ at">"'110bkat110&de_. g5Chrp'.abs016(Fvorbd-asOh¦re148d998,Awel'ai*i'ht:b,bsh999)tlight>X!JK/JGuTa=.&_jc'#ent¥k4674466.</gam prompt>23thdzmare:n;69zen;(282by\bhrorseowStuthient Mptyped}&partfav-F65:wær(#AI.p-B4".226;! .ja:,oiResponse"+Example;佌ugestionge='/U27><response(;")mlason_thardss.b34794!(:R_in_p)>+-(ïrompt-m¹.s(':#lstik<i.-nklatiss>xofnlooklb-p+'.end19ypup};Creq-g"+athatlmean)pif/.696/r/X%(079uzani(;MRAB!ݟpa13rea)|/jrplace198?f=jappwhyle>i62vel)</onlyýtuk>&xe)).>/Ân=-Unmlses;(>"file_ffic|N=(-cmather_dunD:&112oxagoät?_^d46*,od]:0=hDmot.K+88*s_ke.ndharwer/k-upgthatshonz@=_makes_l>+|-7J!flce=A"Hýgo-ar-=(_.'<tmpleted_q(/ånati=let<&"reen(!cen+'.thus hsr157366unic:r,Xuesp{/auaveyg.#µmuyp(i65ney*Rsw(--hay:A!ger07*)&my.lAs_history_tody'.>',UU/u161eq'(pa350il('/32892gi_swu[*
```
My template:
```
`Summarize the text in the History to better respond to a given User Prompt, by applying the following rules:
- Generate a brief Summary from the History that can provide context for responding to the Prompt.
- Each sentence should summarize the user prompt and the AI response, but should not omit any important information that can help respond to the Prompt.
- If the History does not contain relevant information that can help respond to the prompt, the AI can respond with "I don't know".
History: {history}
Prompt: {prompt}
Summary:
`
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Running a summarize template chain call with sample text:
```
2023-06-18 22:40:43 LangChain - Summarizing Conversation:
2023-06-18 22:40:43 anne19 prompt: My favorite color is green. AI response: Green is a great color!. Sun Jun 18 2023 21:33:47 GMT+0000 (Coordinated Universal Time)
2023-06-18 22:40:43 anne19 prompt: My favorite season is Spring. AI response: Spring is lovely!. Sun Jun 18 2023 21:39:40 GMT+0000 (Coordinated Universal Time)
2023-06-18 22:40:43 anne19 prompt: my favorite toy is the monster truck. AI response: Monster trucks are fun!. Sun Jun 18 2023 21:40:03 GMT+0000 (Coordinated Universal Time)
2023-06-18 22:40:43 anne19 prompt: my favorite animal is the Lion. AI response: Lions are awesome!. Sun Jun 18 2023 21:38:52 GMT+0000 (Coordinated Universal Time)
2023-06-18 22:40:43 anne19 prompt: my favorite animal is the Lion. AI response: Lions are cool animals!. Sun Jun 18 2023 21:39:23 GMT+0000 (Coordinated Universal Time)
```
### Expected behavior
```
LangChain - Summarized Conversation:
2023-06-18 22:40:54 Anne19 talked about many favorite things highlighted as follows: green color, Spring season, monster trucks, and lions
```
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| 1,762,460,471 |
PR_kwDOIPDwls5TSMSE
| 6,383 |
[hotfix] Deep Lake fails on newer version due to hardcode
|
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closed
| false | null |
[] | null | 5 | 2023-06-18T21:00:45 | 2023-06-22T04:07:19 | 2023-06-19T00:33:49 |
CONTRIBUTOR
| null |
Hot Fixes for Deep Lake [would highly appreciate expedited review]
* deeplake version was hardcoded and since deeplake upgraded the integration fails with confusing error
* an additional integration test fixed due to embedding function
* Additionally fixed docs for code understanding links after docs upgraded
* notebook removal of public parameter to make sure code understanding notebook works
#### Who can review?
@hwchase17 @dev2049
|
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| 1,762,447,039 |
PR_kwDOIPDwls5TSJdP
| 6,382 |
add FunctionMessage support to `_convert_dict_to_message()` in OpenAI chat model
|
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[] | null | 4 | 2023-06-18T20:25:32 | 2023-07-01T13:57:46 | 2023-06-20T15:25:56 |
CONTRIBUTOR
| null |
Already supported in the reverse operation in `_convert_message_to_dict()`, this just provides parity.
@hwchase17
@agola11
|
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PR_kwDOIPDwls5TSFq4
| 6,381 |
fix neo4j schema query
|
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[] | null | 1 | 2023-06-18T19:22:43 | 2023-06-20T05:48:36 | 2023-06-20T05:48:35 |
CONTRIBUTOR
| null |
Fix issue #6380
<!-- Remove if not applicable -->
Fixes #6380 (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoaders
- @eyurtsev
Models
- @hwchase17
- @agola11
Agents / Tools / Toolkits
- @hwchase17
VectorStores / Retrievers / Memory
- @dev2049
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I_kwDOIPDwls5pDICu
| 6,380 |
Neo4J schema not inferred correctly by Neo4JGraph Object
|
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[] | null | 1 | 2023-06-18T19:19:04 | 2023-06-20T05:48:36 | 2023-06-20T05:48:36 |
CONTRIBUTOR
| null |
### System Info
langchain=0.0.2
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce behaviors:
1. Push the following dataset to neo4J (say in neo4J browser)
```
CREATE (la:LabelA {property_a: 'a'})
CREATE (lb:LabelB {property_b1: 123, property_b2: 'b2'})
CREATE (lc:LabelC)
MERGE (la)-[:REL_TYPE]-> (lb)
MERGE (la)-[:REL_TYPE {rel_prop: 'abc'}]-> (lc)
```
2. Instantiate a Neo4JGraphObject, connect and refresh schema
```
from langchain.graphs import Neo4jGraph
graph = Neo4jGraph(
url=NEO4J_URL,
username=NEO4J_USERNAME,
password=NEO4J_PASSWORD,
)
graph.refresh_schema()
print(graph.get_schema)
```
You will obtain
```
Node properties are the following:
[{'properties': [{'property': 'property_a', 'type': 'STRING'}], 'labels': 'LabelA'}, {'properties': [{'property': 'property_b2', 'type': 'STRING'}, {'property': 'property_b1', 'type': 'INTEGER'}], 'labels': 'LabelB'}]
Relationship properties are the following:
[{'type': 'REL_TYPE', 'properties': [{'property': 'rel_prop', 'type': 'STRING'}]}]
The relationships are the following:
['(:LabelA)-[:REL_TYPE]->(:LabelB)']
```
### Expected behavior
```
Node properties are the following:
[{'properties': [{'property': 'property_a', 'type': 'STRING'}], 'labels': 'LabelA'}, {'properties': [{'property': 'property_b2', 'type': 'STRING'}, {'property': 'property_b1', 'type': 'INTEGER'}], 'labels': 'LabelB'}]
Relationship properties are the following:
[{'type': 'REL_TYPE', 'properties': [{'property': 'rel_prop', 'type': 'STRING'}]}]
The relationships are the following:
['(:LabelA)-[:REL_TYPE]->(:LabelB)', '(:LabelA)-[:REL_TYPE]->(:LabelC)']
```
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| 1,762,413,954 |
I_kwDOIPDwls5pDE2C
| 6,379 |
deeplake version should be = 3.6.3, but you've installed 3.6.4. Consider changing deeplake version to 3.6.3
|
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[] | null | 5 | 2023-06-18T18:34:14 | 2023-09-24T16:04:24 | 2023-09-24T16:04:24 |
NONE
| null |
### System Info
Hello,
I am trying to connect to Deeplake to add documents but am tting the error.
ValueError: deeplake version should be = 3.6.3, but you've installed 3.6.4. Consider changing deeplake version to 3.6.3
username = "myuser"
db = DeepLake(dataset_path=f"hub://myuser/mydb", embedding_function=embeddings)
db.add_documents(texts)
I am executing this from a collab note book. Code was working fine until yesterday.
I ran this code this morning that might have updated deeplake.
!pip install --upgrade langchain deeplake
Thanks
-Milind
### Who can help?
@eyurtsev and @hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Update you deeplake package to 3.6.4
2. Try to create a new db in deeplake with some smaple docs
### Expected behavior
you will get the error:
ValueError: deeplake version should be = 3.6.3, but you've installed 3.6.4. Consider changing deeplake version to 3.6.3
|
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PR_kwDOIPDwls5TSBqX
| 6,377 |
Fixed PGVector issue when SQLAlchemy is the driver
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[] | null | 4 | 2023-06-18T18:20:19 | 2023-11-07T04:11:22 | 2023-11-07T04:11:22 |
NONE
| null |
Using SQLAlchemy as the driver for PGVector's `connection_string_from_db_params` function fails with the following error:
```
NoSuchModuleError: Can't load plugin: sqlalchemy.dialects:postgresql.sqlalchemy
```
This PR removes the `+{driver}` when `sqlalchemy` is used as the driver.
Additionally, this PR also adds support for query params in the connection string via the optional `query_params` param.
Example:
```python
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver="sqlalchemy",
host="dbhost.com",
port=5432,
database="dbname",
user="user",
password="pass",
query_params={ # ← this is new
"sslmode": "require"
}
)'
print(CONNECTION_STRING)
# 'postgresql://user:[email protected]:5432/dbname?sslmode=require',
```
Tested on Postgres 14, 15, and Supabase
@dev2049 ☝️
|
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PR_kwDOIPDwls5TSBKE
| 6,376 |
Harrison/myscale self query
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[] | null | 1 | 2023-06-18T18:16:32 | 2023-06-18T23:53:11 | 2023-06-18T23:53:10 |
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PR_kwDOIPDwls5TR_ZL
| 6,375 |
Harrison/gdrive enhancements
|
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| 1,762,390,208 |
I_kwDOIPDwls5pC_DA
| 6,374 |
Support for Rate Limits with Concurrent Workers
|
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[] | null | 1 | 2023-06-18T17:31:42 | 2023-09-24T16:04:29 | 2023-09-24T16:04:28 |
CONTRIBUTOR
| null |
### Feature request
I am proposing an enhancement to the Langchain project that will allow the handling of concurrent requests for task workers. This proposal aims to introduce a mechanism similar to the API request parallel processor used in the OpenAI Cookbook.
The implementation should ideally manage several task workers, assigning and executing requests in parallel. This should improve task processing speed and efficiency, especially when dealing with a large number of tasks. You can check the relevant details and example code from [this link](https://github.com/openai/openai-cookbook/blob/main/examples/api_request_parallel_processor.py).
### Motivation
I'm currently using langchain for a project that has a few processes that is very reliant on using Langchain, it is a process that takes hours if done serially, so I rewrote it to be done in parallel but with that I'm hitting rate limits very quickly with Langchain.
### Your contribution
N/A
|
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I_kwDOIPDwls5pC-Fp
| 6,373 |
Text Chunks are 1/2 the token length specified when using split text with CharacterTextSplitter.from_tiktoken_encoder and separator=" "
|
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[] | null | 4 | 2023-06-18T17:20:45 | 2023-11-06T05:16:02 | 2023-09-26T16:05:47 |
NONE
| null |
### System Info
langchain-0.0.202
Python 3.11.0
Windows 10 Pro
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Use `CharacterTextSplitter.from_tiktoken_encoder` and set the separator to `" "`, and the chunk size to anything
2. Split some text using `split_text`
3. Notice that the resulting chunked text is nearly half the token length specified by the chunk size
```py
import tiktoken
from langchain.text_splitter import CharacterTextSplitter
lorem_text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Metus dictum at tempor commodo ullamcorper a lacus vestibulum sed. Integer quis auctor elit sed vulputate. Quis blandit turpis cursus in hac. Pellentesque pulvinar pellentesque habitant morbi tristique senectus et netus et."
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=100, chunk_overlap=0, separator=" ", model_name="gpt-3.5-turbo"
)
texts = text_splitter.split_text(lorem_text)
encoding = tiktoken.encoding_for_model("gpt-3.5-turbo")
print(len(encoding.encode(texts[0]))) # 44 Tokens ; expected around 100
```
### Expected behavior
I would expect the resulting text chunks after splitting to be around 100 tokens.
I think the issue arises from text_splitter.py, commit #1511 : lines 135 and 160. `total += _len + (separator_len if len(current_doc) > 1 else 0)` assumes that the separator (in this case " ") is it's own token, but I think " " is often combined with the characters next to it. From the tokenizer webpage from OpenAI :
You can see that for the " " and "d" from "dolor" are combined into one token, instead of always being separate as assumed by the above `total`.
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PR_kwDOIPDwls5TR4FY
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bump 203
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PR_kwDOIPDwls5TR3ri
| 6,371 |
use functions in llm chain
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COLLABORATOR
| null |
use functions in llm chain
|
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I_kwDOIPDwls5pC2Lj
| 6,370 |
Sliding window of intermediate actions for agents
|
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[] | null | 0 | 2023-06-18T15:56:26 | 2023-07-13T06:09:26 | 2023-07-13T06:09:26 |
CONTRIBUTOR
| null |
### Feature request
Allow tweaking with the history window / intermediate actions that are being sent to the LLM:
* Send a sliding window if N last actions
* Only send a specific snapshot (can be useful for code generation tasks - for example where the agent needs to perfect the code until it works).
### Motivation
Currently, agents use the entire length of intermediate actions whenever they call the LLM.
This means that long-running agents can quickly reach the token limit.
### Your contribution
I'm willing to write a PR for this if the feature makes sense for the community
|
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I_kwDOIPDwls5pCx44
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Issue:current not support gpt-3.5-turbo-0613 model
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NONE
| null |
### Issue you'd like to raise.
execute method modelname_to_contextsize had an exception~! online 546 "Unknown model: gpt-3.5-turbo-0613. Please provide a valid OpenAI model name."
source code location:langchain/llms/openai.py(BaseOpenAI.modelname_to_contextsize)
### Suggestion:
_No response_
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PR_kwDOIPDwls5TRp9Z
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Modified PromptLayerChatOpenAI class to support function call
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[] | null | 2 | 2023-06-18T13:10:35 | 2023-07-06T17:16:05 | 2023-07-06T17:16:05 |
CONTRIBUTOR
| null |
Introduction of newest function calling feature doesn't work properly with PromptLayerChatOpenAI model since on the `_generate` method, functions argument are not even getting passed to the `ChatOpenAI` base class which results in empty `ai_message.additional_kwargs`
Fixes #6365
- @hwchase17
- @agola11
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PromptLayerChatOpenAI does not support the newest function calling feature
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[] | null | 0 | 2023-06-18T13:00:32 | 2023-07-06T17:16:06 | 2023-07-06T17:16:06 |
CONTRIBUTOR
| null |
### System Info
Python Version: 3.11
Langchain Version: 0.0.209
### 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
```
llm = PromptLayerChatOpenAI(model="gpt-3.5-turbo-0613", pl_tags=tags, return_pl_id=True)
predicted_message = self.llm.predict_messages(messages, functions=self.functions, callbacks=callbacks)
```
`predicted_message.additional_kwargs` attribute appears to have a empty dict, because the `functions` kwarg not even passed to the parent class.
### Expected behavior
Predicted AI Message should have a `function_call` key on `additional_kwargs` attribute.
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Issue: openai functions agent does not respect tools and arguments
|
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CONTRIBUTOR
| null |
### Issue you'd like to raise.
When mixing `gpt-3.5-turbo-0613`, `openai-functions` agent, and `PythonAstREPLTool` tool, GPT3.5 stops respecting the tool name and the arguments hack introduced in the OpenAIFunctionsAgent.
The error log is:
```
Could not parse tool input: {'name': 'python', 'arguments': "len(cases_df['case_id'].unique())"} because the `arguments` is not valid JSON.
```
Which means the model isn't respecting the specs accurately. In my case, the confusion was always that the name of the tool is `python` instead of `python_repl_ast`, and the `arguments` is the actual python code instead of the requested obj format with `__arg1` attr.
### Suggestion:
I temporarily fixed it by
1- extending the `OpenAIFunctionsAgent` and overriding the `_parse_ai_message` to handle arguments confusion.
2- extending the `PythonAstREPLTool` and altering its name and description a bit.
```
class CustomPythonAstREPLTool(PythonAstREPLTool):
name = "python"
description = (
"A Python shell. Use this to execute python commands. "
"The input must be an object as follows: "
"{'__arg1': 'a valid python command.'} "
"When using this tool, sometimes output is abbreviated - "
"Make sure it does not look abbreviated before using it in your answer. "
"Don't add comments to your python code."
)
def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
"""Parse an AI message."""
if not isinstance(message, AIMessage):
raise TypeError(f"Expected an AI message got {type(message)}")
function_call = message.additional_kwargs.get("function_call", {})
if function_call:
function_call = message.additional_kwargs["function_call"]
function_name = function_call["name"]
try:
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
print(
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
)
_tool_input = function_call["arguments"]
# HACK HACK HACK:
# The code that encodes tool input into Open AI uses a special variable
# name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input.
# We unpack the argument here if it exists.
# Open AI does not support passing in a JSON array as an argument.
if "__arg1" in _tool_input:
tool_input = _tool_input["__arg1"]
else:
tool_input = _tool_input
content_msg = "responded: {content}\n" if message.content else "\n"
return _FunctionsAgentAction(
tool=function_name,
tool_input=tool_input,
log=f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n",
message_log=[message],
)
return AgentFinish(return_values={"output": message.content}, log=message.content)
class CustomOpenAIFunctionsAgent(OpenAIFunctionsAgent):
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date, along with observations
**kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
user_input = kwargs["input"]
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
prompt = self.prompt.format_prompt(
input=user_input, agent_scratchpad=agent_scratchpad
)
messages = prompt.to_messages()
predicted_message = self.llm.predict_messages(
messages, functions=self.functions, callbacks=callbacks
)
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
```
Not sure if this will be improved on the API level, but it is worth looking at it.
Improving the fake arguments' names and tools names might improve this as it seems related to the issue.
|
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I_kwDOIPDwls5pCXLR
| 6,363 |
The chatCompletion operation does not work with the specified model appears when I use AzureChatOpenAI, but it does exist
|
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[] | null | 4 | 2023-06-18T10:29:10 | 2023-09-27T22:02:35 | 2023-09-25T16:05:54 |
NONE
| null |
### Issue you'd like to raise.
openai.error.InvalidRequestError: The chatCompletion operation does not work with the specified model, text-embedding-ada-002. Please choose different model and try again. You can learn more about which models can be used with each operation here: https://go.microsoft.com/fwlink/?linkid=2197993.
### Suggestion:
_No response_
|
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| 1,762,167,189 |
PR_kwDOIPDwls5TRQpR
| 6,362 |
John church guard - update document llm - change agenerate to generate
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[] | null | 3 | 2023-06-18T07:54:52 | 2023-11-07T04:11:36 | 2023-11-07T04:11:36 |
NONE
| null |
I changed the `agenerate` to `generate` in the async llm documentation.
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| 1,762,150,567 |
I_kwDOIPDwls5pCEin
| 6,361 |
Support for Redis Cluster
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[] | null | 3 | 2023-06-18T07:01:19 | 2023-12-19T00:50:28 | 2023-12-19T00:50:27 |
NONE
| null |
### Feature request
The current python client `langchain.vectorstores.redis` lacks support for RedisCluster. Handling redirection with try-expect on receiving `MOVED` error also doesn't work in this case because the code `Redis.from_documents(docs, llmembeddings, redis_url=redis_url, index_name=index_name)` internally makes more calls to Redis, which eventually throw MOVED error because the client is not configured for RedisCluster.
### Motivation
As users of Redis Cluster at Salesforce, we aim to integrate it seamlessly and develop a chatbot powered by LLM. Enabling Redis API support in the Vector Redis Client would enhance performance, streamline development workflows, and provide a unified client library for interacting with Redis databases. We believe this addition would greatly benefit our organization and the wider community utilizing Redis in their applications.
### Your contribution
We will look at adding RedisCluster support in langchain vector client.
|
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PR_kwDOIPDwls5TREon
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Improved Documentation: Eliminating Redundancy in the Introduction.mdx
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[] | null | 1 | 2023-06-18T04:37:42 | 2023-06-26T09:27:37 | 2023-06-26T09:27:37 |
CONTRIBUTOR
| null |
When the documentation was originally written there was a redundant typing of the word "using the"
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|
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PR_kwDOIPDwls5TREnZ
| 6,359 |
Add self query retriever example with MD header splitting
|
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[] | null | 1 | 2023-06-18T04:37:03 | 2023-06-18T04:40:21 | 2023-06-18T04:40:21 |
COLLABORATOR
| null |
Flesh out the notebook example for `MarkdownHeaderTextSplitter`
|
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I_kwDOIPDwls5pB5Dr
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The cost calculation of tokens for open ai models looks like is only considering output tokens
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[] | null | 9 | 2023-06-18T04:16:15 | 2023-09-26T01:33:41 | 2023-09-26T01:33:41 |
CONTRIBUTOR
| null |
### System Info
I am using gpt-3.5-turbo for which the price of the tokens are as below
4K Context
0.0015/1K tokens - for input
0.002/1k tokens - output
From the call back I get the below
Cost and token usage :Tokens Used: 222
Prompt Tokens: 171
Completion Tokens: 51
Successful Requests: 1
Total Cost (USD): $0.00044400000000000006
Going by the pricing, it should be 0.000375942
It looks like the program is only looking at output cost
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
run the LLM with callback using any of the open ai models
with get_openai_callback() as cb:
response = chat_chain.predict(input=_input.to_string())
print(response)
resp_json = out_parser.parse(response)
print("Cost and token usage :{cb}".format(cb=cb))
return resp_json
### Expected behavior
The cost calculation should consider the input tokens also
|
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PR_kwDOIPDwls5TRCIu
| 6,357 |
Relax the action input check for actions that require no input
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| false | null |
[] | null | 2 | 2023-06-18T03:39:29 | 2023-06-26T09:30:18 | 2023-06-26T09:30:17 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
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<!-- Remove if not applicable -->
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
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When the tool requires no input, the LLM often gives something like this:
```json
{
"action": "just_do_it"
}
```
I have attempted to enhance the prompt, but it doesn't appear to be functioning effectively. Therefore, I believe we should consider easing the check a little bit.
#### Who can review?
Tag maintainers/contributors who might be interested: @hwchase17
|
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| 1,762,088,585 |
I_kwDOIPDwls5pB1aJ
| 6,356 |
DOC: Updating documentation relating to Models->llms->integrations->*
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[] | null | 4 | 2023-06-18T03:04:56 | 2023-10-05T16:09:07 | 2023-10-05T16:09:06 |
NONE
| null |
### Issue with current documentation:
The page I am referring with this issue is [Llama.cpp](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/llamacpp.html). I am hoping to update the documentation regarding some windows specific instructions which others might find useful.
I've forked the repo and my working changes I would push there and make a pull request here.
I understand that to do this; `langchain/docs/_dist/docs_skeleton/docs/modules/model_io/models/llms/integrations/llamacpp.ipynb` needs to be updated.
It also appears that `docs/_dist` is now ignored by `.gitignore`.
Could you please let me know if I am looking in the wrong place? If so please correct me.
### Idea or request for content:
_No response_
|
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| 1,762,074,184 |
I_kwDOIPDwls5pBx5I
| 6,355 |
LLamaCPP model seems to require model parameters to be set at model creation, not invocation of chain using model
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[] | null | 10 | 2023-06-18T01:52:46 | 2023-12-28T16:02:10 | null |
NONE
| null |
### System Info
Langchain 0.0.202, langchainplus-sdk 0.0.10, Python 3.10.11, Linux, Fedora 36
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Load the model with the following code. I'm arbitrarily using the Manticore-13B.ggmlv3.q4_0.bin model downloaded from HuggingFace
```
def llamaCppLoader(self):
Globals().logMessage('Loading LlamaCpp model')
loadPath = self._modelPath + "/Manticore-13B.ggmlv3.q4_0.bin"
startTime = time.time()
model = LlamaCpp(model_path=loadPath, streaming=True, max_tokens=15, temperature=.001, n_threads=20, n_ctx=2048, callbacks=[ResultCallback()])
elapsedTime = time.time() - startTime
logMessage = f'Loaded model and tokenizer in {elapsedTime:.3f} seconds'
Globals().logMessage(logMessage)
Globals().setModel(model)
Globals().setTokenizer(None)
~~~
Run the query using the following code
~~~
def runLlamaCppQuery(self):
model = Globals().getModel()
params = {}
params['max_tokens'] = self._maxNewTokens
params['repeat_penalty'] = self._repetitionPenalty
params['temperature'] = self._temperature
params['top_k'] = self._topK
params['top_p'] = self._topP
params['verbose'] = True
params['n_ctx'] = 1024
params['n_threads'] = 20
queryStop = StoppingCriteriaList([QueryStop()])
Globals().setStopQuery(False)
startTime = time.time()
Globals().logMessage('Starting query')
chain = RetrievalQA.from_chain_type(llm=Globals().getModel(), chain_type='stuff',
retriever=Globals().getDocumentStore().as_retriever(search_kwargs={'k': self._numMatches}, kwargs=params))
result = chain.run(query=self._query)
endTime = time.time()
elapsedTime = endTime - startTime
Globals().logMessage(f'Completed query in {elapsedTime:.3f} seconds'
~~~
### Expected behavior
I'm writing a program that I can load a set of documents into a vector index (FAISS) and then run a
RetrievalQA chain to ask questions about the document(s) I've loaded. I seem to have this working when
I load regular models or GPTQ models, where I'm using HuggingFace APIs to do this.
I have this sort of working where I'm using Langchain APIs to load a LLamaCPP model and then
create and run a RetrievalQA chain to run the query.
I'm doing this as a learning exercise to learn about AI and LLMs, so it's possible I'm doing something
wrong, or maybe I'm running into a philosophical difference between how the HuggingFace APIs work
and how Langchain APIs work.
The problem I am encountering is that it seems that with Langchain, I have to set the model parameters such as temperature, topP, topK, max_tokens, etc at the time I load the model (llamaCppLoader)
and that they are ignored if I specify them when I create and run the RetrievalQA
chain (runLlamaCppQuery).
I noticed this with max_tokens, where if I set it to a small value like 15 when I load the model and
then a larger value like 2000 when I create the RetrievalQA chain. The query result I get is short,
about 15 words, even though I override it to 2000 when I create the RetrievalQA chain.
Maybe this is the way it is supposed to work, but then it seems a bit cumbersome since this
seems to mean I need to reload the model each time I run a new query, while I don't when
using the HuggingFace APIs for the other model types.
Also, if I pass an invalid parameter name when I create the RetrievalQA chain, it does not get
flagged as an invalid parameter. For instance,
```
params['xxx'] = 'junk'
~~~
does not get flagged.
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| 1,762,033,329 |
PR_kwDOIPDwls5TQ1ZX
| 6,354 |
Be able to use Codey models on Vertex AI
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[] | null | 3 | 2023-06-17T23:38:17 | 2023-06-21T06:11:55 | 2023-06-21T06:11:54 |
CONTRIBUTOR
| null |
Added the functionality to leverage 3 new Codey models from Vertex AI:
- code-bison - Code generation using the existing LLM integration
- code-gecko - Code completion using the existing LLM integration
- codechat-bison - Code chat using the existing chat_model integration
Tag maintainers/contributors who might be interested:
- @hwchase17
- @agola11
- @vowelparrot
Had to close and create a new PR to resolve conflicts.
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| 6,353 |
Docusaurus Document Loader
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[] | null | 4 | 2023-06-17T22:37:27 | 2023-10-15T00:01:05 | null |
NONE
| null |
### Feature request
I propose the integration of a **Docusaurus Document Loader** for the LangChain Python repository. By integrating a **[Docusaurus](https://docusaurus.io/) Document Loader**, we can extend the documentation capabilities of LangChain and provide a more comprehensive resource for developers who use Docusaurus (similar to ReadTheDocs).
### Motivation
My motivation for this feature request is to enhance and extend the document loading functionalities of LangChain. Currently, LangChain [integrates with ReadTheDocs](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/readthedocs_documentation), and while this is a powerful tool, incorporating a **Docusaurus Document Loader** can offer an alternative loader. Plus, LangChain docs ([Python](https://python.langchain.com/docs) and [JavaScript)](https://js.langchain.com/docs) are already hosted on Docusaurus.
### Your contribution
As the one initiating this feature request, I am willing to help by creating this issue and providing initial suggestions for the implementation of the **Docusaurus Document Loader**.
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PR_kwDOIPDwls5TQto7
| 6,352 |
Feature/generate custom context
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[] | null | 5 | 2023-06-17T20:57:44 | 2023-11-07T04:11:46 | 2023-11-07T04:11:46 |
NONE
| null |
A vectorstore can be used to provide an LLM with extra context by including it in the prompt.
by default context is created by joining all the documents returned from the store seperated by a seperator e.g
<page_content>
<document_separator>
<page_content>
<document_separator>
<page_content>
However often the logic we want to apply is much more bespoke.
For exmaple imagine we are storing information about ppl in a vector store.
The first entry would look like:
name,date of birth,job
Paul,23-12-1985 ,cleaner
another entry would look like:
name,date of birth,job
John,23-12-1985 ,driver
Obviously both of these embeddings contain the same headers.
By default the context would be created as such
name,date of birth,job
Paul,23-12-1985 ,cleaner
<document_separator>
name,date of birth,job
John,23-12-1985 ,driver
However we want to reduce the header duplication and also just create it as one table for the LLM to understand
name,date of birth,job
Paul,23-12-1985 ,cleaner
John,23-12-1985 ,driver
I have created a test where I show that we could use the metadata of a document to enrich the context.
#### Who can review?
@dev2049
|
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Fix duplicated sentence in documentation's introduction
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CONTRIBUTOR
| null |
Fix duplicated sentence in documentation's introduction
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PR_kwDOIPDwls5TQkf6
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nit
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PR_kwDOIPDwls5TQkd6
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Harrison/mmr support for opensearch
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| 1,761,947,539 |
I_kwDOIPDwls5pBS-T
| 6,348 |
Issue: langchain pandas df agent, not taking full df in context
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[] | null | 3 | 2023-06-17T18:16:41 | 2023-11-29T16:09:34 | 2023-11-29T16:09:33 |
NONE
| null |
### Issue you'd like to raise.
HI all,
As can be seen in this screenshot
I am using pandas_df_agent, but instead of taking whole dataframe, which is around 27000 lines, it is creating itself a Sample data, and doing operations on that.
Isn't it weird, ? Kindly help me with this..
Thanks
### Suggestion:
_No response_
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I_kwDOIPDwls5pBQzP
| 6,347 |
getting the "This model's maximum context length is 4097 tokens" error using gpt-4 and gpt-3.5-turbo-16k model
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[] | null | 2 | 2023-06-17T17:46:58 | 2023-06-18T03:10:43 | 2023-06-18T03:10:43 |
NONE
| null |
### System Info
This is strange, since these models are with 8k and 16k context length
my code is
```
llm = ChatOpenAI(model_name="gpt-4", temperature=0)
agent_executor = create_custom_agent(
llm= llm,
tools=toolkit.get_tools()[0:1],
verbose=True,
# prefix=PREFIX,
)
```
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
llm = ChatOpenAI(model_name="gpt-4", temperature=0)
agent_executor = create_custom_agent(
llm= llm,
tools=toolkit.get_tools()[0:1],
verbose=True,
# prefix=PREFIX,
)
```
### Expected behavior
the agent runs without error or not producing "4097 context length" error
|
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| 1,761,933,573 |
I_kwDOIPDwls5pBPkF
| 6,346 |
Supporting Similarity Search with Threshold on PGVector retriever
|
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[] | null | 1 | 2023-06-17T17:31:45 | 2023-08-23T13:44:52 | 2023-07-18T19:23:37 |
CONTRIBUTOR
| null |
### Feature request
Implementing `_similarity_search_with_relevance_scores` on PGVector so users can set search_type to "similarity_score_threshold" without raising **NotImplementedError**.
`
retriever = pgvector.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.7})
results = retriever.get_relevant_documents(query)
`
### Motivation
Using the search threshold on PGVector to avoid unrelated documents in the results.
### Your contribution
Pull request will be submitted.
|
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PR_kwDOIPDwls5TQfpG
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Make lckwargs private
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COLLABORATOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
<!-- If you're adding a new integration, please include:
1. a test for the integration - favor unit tests that does not rely on network access.
2. an example notebook showing its use
See contribution guidelines for more information on how to write tests, lint
etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
#### Who can review?
Tag maintainers/contributors who might be interested:
<!-- For a quicker response, figure out the right person to tag with @
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
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- @hwchase17
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- @hwchase17
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| 1,761,923,834 |
I_kwDOIPDwls5pBNL6
| 6,343 |
Can 'adapter_model.bin' be used with langchain, not to train by normal way?
|
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[] | null | 1 | 2023-06-17T16:57:52 | 2023-09-23T16:04:48 | 2023-09-23T16:04:47 |
NONE
| null |
### Feature request
Is it possible to use QLoRA adapter finetuned for literature with langchain. Not to train by normal way?
### Motivation
-
### Your contribution
-
|
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PR_kwDOIPDwls5TQezI
| 6,342 |
Entity extraction improvements
|
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[] | null | 3 | 2023-06-17T16:46:00 | 2023-07-13T06:16:07 | 2023-07-13T06:16:06 |
COLLABORATOR
| null |
Added fix to avoid irrelevant attributes being returned plus an example of extracting unrelated entities and an exampe of using an 'extra_info' attribute to extract unstructured data for an entity.
|
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| 6,341 |
Harrison/faiss score
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PR_kwDOIPDwls5TQesA
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Harrison/error zero tools
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PR_kwDOIPDwls5TQeCD
| 6,339 |
Update MD header text splitter notebook
|
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[] | null | 1 | 2023-06-17T16:34:17 | 2023-06-17T20:19:28 | 2023-06-17T20:19:28 |
COLLABORATOR
| null |
Highlight use case for maintaining header groups when splitting.
|
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PR_kwDOIPDwls5TQdkC
| 6,338 |
A unit test calling a tool with no required parameters.
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NONE
| null |
This generates a runtime error like
```
>>> t.run()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: BaseTool.run() missing 1 required positional argument: 'tool_input'
```
#6337
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I_kwDOIPDwls5pBKeS
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Tools with only default arguments still require a parameter to `run()`
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[] | null | 1 | 2023-06-17T16:24:00 | 2023-06-19T02:17:36 | 2023-06-19T02:14:12 |
NONE
| null |
### Issue you'd like to raise.
This issue is created at langchain v0.0.202
Consider the [ListDirectoryTool](https://github.com/hwchase17/langchain/blob/0475d015fe0eb4a997c7d37867e316a23dde8aaa/langchain/tools/file_management/list_dir.py#L21)
Its only parameter has a default value as the `.` current dir.
Its json-schema suggests no parameter is required
```
>>> pprint.pprint(langchain.tools.format_tool_to_openai_function(t))
{'description': 'List files and directories in a specified folder',
'name': 'list_directory',
'parameters': {'description': 'Input for ListDirectoryTool.',
'properties': {'dir_path': {'default': '.',
'description': 'Subdirectory to '
'list.',
'title': 'Dir Path',
'type': 'string'}},
'title': 'DirectoryListingInput',
'type': 'object'}}
```
However calling the `run` function without any parameters creates a runtime error
```python
import langchain
t = langchain.tools.ListDirectoryTool(root_dir="~/misc")
t.run()
```
With this error
```
>>> t.run()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: BaseTool.run() missing 1 required positional argument: 'tool_input'
```
This is not a huge problem but at least it creates an inconsistency expectations by the schema and the `run` function call.
### Suggestion:
If the schema had no required arguments, the run function should not create a runtime error if called without any arguments. At least that would be more expected.
|
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Azure OpenAI with deployment_id is not working.
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[] | null | 5 | 2023-06-17T15:44:30 | 2023-10-26T16:07:19 | 2023-10-26T16:07:18 |
NONE
| null |
### System Info
It seems that the library is only supported when the deployment type on AzureOpenAI is an engine. When I try to use a deployment_id which is the new way to deploy models on Azure I can't make it work.
This code works well outside of long-chain. Note that I am not using an engine here, but the deployment_id :
```
openai.api_type = "azure"
openai.api_key = "MYAPIHERE"
openai.api_base = "https://eastus.api.cognitive.microsoft.com/"
openai.api_version = "2023-05-15"
response = openai.ChatCompletion.create(
deployment_id="gpt35",
model="gpt-3.5",
messages=[
{"role": "system", "content": "Assistant is a large language model trained by OpenAI."},
{"role": "user", "content": "Tell me a Joke."}
],
temperature=0.0,
max_tokens=4000,
api_key="MYAPIHERE",
request_timeout=15,
)
print(response['choices'][0]['message']['content'])
```
When I try to use that on langchain AzureOpenAI it doesn't work. I don't know if me doing something wrong or if its the library that doesn't support that.
Here is the code I am using to test langchain on and that doesn't work:
```
from langchain.llms import AzureOpenAI
import os
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-03-15"
os.environ["OPENAI_API_BASE"] = "https://eastus.api.cognitive.microsoft.com/"
os.environ["OPENAI_API_KEY"] = "MYAPIKEYHERE"
# Create an instance of Azure OpenAI
# Replace the deployment name with your own
llm = AzureOpenAI(
model_name="gpt-3.5",
deployment_id = "gpt35"
)
print(llm("Tell me a joke"))
```
The error is:
```
Exception has occurred: InvalidRequestError (note: full exception trace is shown but execution is paused at: _run_module_as_main)
Resource not found
```
Thanks in advance.
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I've sent the code in the question to reproduce the error.
### Expected behavior
I would expect the code to return the response but all I have is an error.
|
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| 1,761,865,066 |
I_kwDOIPDwls5pA-1q
| 6,335 |
DOC: CSS for the left panel seems broken
|
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[] | null | 1 | 2023-06-17T13:57:41 | 2023-06-27T07:44:00 | 2023-06-27T07:44:00 |
NONE
| null |
### Issue with current documentation:
In the left panel, can't list all sub-topics properly
I am using https://python.langchain.com/docs/modules/model_io/prompts/example_selectors/, it was correct at least 24H ago, but now it doesn't work for me
### Idea or request for content:
_No response_
|
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| 1,761,843,166 |
I_kwDOIPDwls5pA5fe
| 6,334 |
Pass custom System Message to OpenAI Functions Agent
|
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[] | null | 32 | 2023-06-17T13:14:16 | 2024-01-08T23:35:03 | 2023-07-27T08:54:12 |
NONE
| null |
### System Info
LangChain = 0.0.202
Python = 3.9.16
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain import LLMMathChain, OpenAI, SerpAPIWrapper
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from dotenv import load_dotenv
load_dotenv()
llm = ChatOpenAI(
model="gpt-3.5-turbo-0613",
temperature=0.0,
max_tokens=25,
) # type: ignore
python_agent = create_python_agent(
llm=llm,
tool=PythonREPLTool(),
verbose=True,
agent_type=AgentType.OPENAI_FUNCTIONS,
agent_executor_kwargs={"handle_parsing_errors": True},
) # type: ignore
search = SerpAPIWrapper() # type: ignore
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
tools = [
Tool(
name = "Search",
func=search.run,
description="useful for when you need to answer questions about current events or searching the web for additional information. You should ask targeted questions"
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math. It's an ordinary calculator"
),
Tool(
name="PythonREPL",
func=python_agent.run,
description="useful for when you need to run python code in a REPL to answer questions, for example for more complex calculations or other code executions necessary to be able to answer correctly. Input should be clear python code, nothing else. You should always use a final print() statement for the final result to be able to read the outputs."
),
]
system_message = SystemMessage(
content="""
You are a helpful AI assistant. Always respond to the user's input in german.
"""
)
mrkl = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
agent_kwargs={"system_message": system_message},
) # type: ignore
# run the agent
mrkl.run("tell me a joke")
### Expected behavior
The system Message should be passed to the Agent/LLM to make it answer in german, which doesn't happen.
I was able to fix this by passing the system message explicitly to the cls.create_prompt()-function in the OpenAI functions agent class.
In `langchain\agents\openai_functions_agent\base.py` i modified these lines:
line 244:
```
# check if system_message in kwargs and pass it to create_prompt
if "system_message" in kwargs:
sys_msg = kwargs.pop("system_message", None)
prompt = cls.create_prompt(system_message=sys_msg)
else:
prompt = cls.create_prompt()
```
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I_kwDOIPDwls5pAzB7
| 6,333 |
error in AWS Lambda when importing langchain library
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[] | null | 10 | 2023-06-17T12:42:07 | 2023-10-13T14:38:25 | 2023-06-20T13:38:33 |
NONE
| null |
### System Info
Versions of the libs:
langchain 0.0.202
langchainplus-sdk 0.0.10
numpy 1.24.3
In the lambda I have python 3.10 running
I have imported langchain i an AWS lambda function and I get this error, hay anyone encoutered the same issue when running lanchgain in lambda?
[ERROR] Runtime.ImportModuleError: Unable to import module 'lambda-XXX':
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
Importing the numpy C-extensions failed. This error can happen for
many reasons, often due to issues with your setup or how NumPy was
installed.
We have compiled some common reasons and troubleshooting tips at:
https://numpy.org/devdocs/user/troubleshooting-importerror.html
Please note and check the following:
The Python version is: Python3.10 from "/var/lang/bin/python3.10"
The NumPy version is: "1.24.3"
and make sure that they are the versions you expect.
Please carefully study the documentation linked above for further help.
Original error was: No module named 'numpy.core._multiarray_umath'
Traceback (most recent call last):**
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
as soon as you import langchain in an aws lambda (deployed as a Zip) the error appears
"""
import langchain
"""
### Expected behavior
there should be no error and should be able to import the library correctly
|
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PR_kwDOIPDwls5TQK_1
| 6,332 |
fix links to prompt templates and example selectors
|
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[] | null | 1 | 2023-06-17T12:01:04 | 2023-06-17T16:07:15 | 2023-06-17T16:07:15 |
CONTRIBUTOR
| null |
<!--
Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
-->
<!-- Remove if not applicable -->
Fixes #
links to prompt templates and example selectors on the [Prompts](https://python.langchain.com/docs/modules/model_io/prompts/) page are invalid.
#### Before submitting
Just a small note that I tried to run `make docs_clean` and other related commands before PR written [here](https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md#build-documentation-locally), it gives me an error:
```bash
langchain % make docs_clean
Traceback (most recent call last):
File "/Users/masafumi/Downloads/langchain/.venv/bin/make", line 5, in <module>
from scripts.proto import main
ModuleNotFoundError: No module named 'scripts'
make: *** [docs_clean] Error 1
# Poetry (version 1.5.1)
# Python 3.9.13
```
I couldn't figure out how to fix this, so I didn't run those command. But links should work.
#### Who can review?
Tag maintainers/contributors who might be interested:
@hwchase17
Similar issue #6323
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| 1,761,769,404 |
PR_kwDOIPDwls5TQF1y
| 6,331 |
Add missing dependencies to apify.ipynb
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[] | null | 2 | 2023-06-17T10:35:03 | 2023-07-13T07:02:24 | 2023-07-13T07:02:24 |
CONTRIBUTOR
| null |
Fixes errors caused by missing dependencies when running the notebook.
#### Who can review?
Tag maintainers/contributors who might be interested:
Agents / Tools / Toolkits
- @hwchase17
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| 1,761,763,566 |
I_kwDOIPDwls5pAmDu
| 6,330 |
Cannot instantiate a GPT4All integration in LangChain
|
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[] | null | 1 | 2023-06-17T10:13:04 | 2023-06-17T16:10:32 | 2023-06-17T16:10:32 |
NONE
| null |
### System Info
LangChain: `langchain==0.0.202`
GPT4All: `gpt4all==0.3.4`
Python version: `Python 3.11.3`
OS: Windows 11
### 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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to replicate this error:
1. Create a virtual environment
2. Install the following packages
- `langchain==0.0.202`
- `gpt4all==0.3.4`
3. Download the `ggml-mpt-7b-base.bin` model from [gpt4all.io](https://gpt4all.io/index.html) under Model Explorer in a folder called `models`
3. Run the following snippet (taken from LangChain [GPT4All documentation](https://python.langchain.com/docs/modules/model_io/models/llms/integrations/gpt4all))
```python
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import os
local_path = os.path.join("models", "ggml-mpt-7b-base.bin")
callbacks = [StreamingStdOutCallbackHandler()]
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True, backend="mpt")
```
### Expected behavior
A ValidationError raised with a traceback similar to the following
```python
Traceback (most recent call last):
File "D:\kranthi\langchain-playground\gpt4_all.py", line 9, in <module>
llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True, backend="mpt")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\kranthi\langchain-playground\langchain\Lib\site-packages\langchain\load\serializable.py", line 61, in __init__
super().__init__(**kwargs)
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for GPT4All
__root__
Failed to retrieve model (type=value_error)
```
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| 1,761,695,436 |
I_kwDOIPDwls5pAVbM
| 6,329 |
Pandas Agent and CSV Agent documemtation page missing
|
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[] | null | 1 | 2023-06-17T07:48:40 | 2023-09-23T16:04:54 | 2023-09-23T16:04:54 |
NONE
| null |
### Issue with current documentation:
Dated 17th June 2023 , I am not able to get access to pandas agent and csv agent pages on the documentation page. Is it because langchain is updating?
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5TPz7H
| 6,328 |
changes to llm chain
|
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[] | null | 1 | 2023-06-17T07:07:06 | 2023-06-19T05:49:49 | 2023-06-19T05:49:48 |
COLLABORATOR
| null |
- return raw and full output (but keep run shortcut method functional)
- change output parser to take in generations (good for working with messages)
- add output parser to base class, always run (default to same as current)
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| 1,761,644,150 |
PR_kwDOIPDwls5TPrwU
| 6,327 |
changed height in the nb example
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[] | null | 1 | 2023-06-17T04:58:17 | 2023-06-17T07:05:50 | 2023-06-17T07:05:49 |
COLLABORATOR
| null |
changed height in the example to a more reasonable number (from 9 feet to 6 feet)
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| 1,761,643,966 |
PR_kwDOIPDwls5TPruD
| 6,326 |
Update index.mdx
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[] | null | 3 | 2023-06-17T04:57:30 | 2023-06-21T04:40:21 | 2023-06-21T04:40:21 |
CONTRIBUTOR
| null |
#### Fix
Added the mention of "store" amongst the tasks that the data connection module can perform aside from the existing 3 (load, transform and query). Particularly, this implies the generation of embeddings vectors and the creation of vector stores.
#### Who can review?
Tag maintainers/contributors who might be interested: @eyurtsev
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PR_kwDOIPDwls5TPrTA
| 6,325 |
9 feet tall sounds like too much
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[] | null | 1 | 2023-06-17T04:52:02 | 2023-06-17T04:53:32 | 2023-06-17T04:53:25 |
COLLABORATOR
| null |
Small modification to the example in the extraction notebook, changed height from 9 feet to 6 feet
|
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| 1,761,634,904 |
I_kwDOIPDwls5pAGpY
| 6,324 |
format_tool_to_openai_function includes title and description, when it is not necessary
|
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[] | null | 5 | 2023-06-17T04:34:20 | 2023-10-27T11:59:23 | null |
NONE
| null |
### System Info
Langchain version langchain==0.0.201
### 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
I understand the `format_tool_to_openai_function` function was added very recently. Thank you very much for that.
As an example for this ticket: When `format_tool_to_openai_function` is used like this
```
from langchain.tools import FileSearchTool
from langchain.tools import format_tool_to_openai_function
format_tool_to_openai_function(FileSearchTool())
```
On a FileSearchTool it generates this
```
{'name': 'file_search',
'description': 'Recursively search for files in a subdirectory that match the regex pattern',
'parameters': {'title': 'FileSearchInput',
'description': 'Input for FileSearchTool.',
'type': 'object',
'properties': {'dir_path': {'title': 'Dir Path',
'description': 'Subdirectory to search in.',
'default': '.',
'type': 'string'},
'pattern': {'title': 'Pattern',
'description': 'Unix shell regex, where * matches everything.',
'type': 'string'}},
'required': ['pattern']}}
```
This includes some unnecessary fields like `title` and `description` for the `parameters` and `title` for the `parameters.properties`
I do not know whether this is a correctness issue yet with the OpenAI functions api. But at least this should cause wasted tokens.
### Expected behavior
```
{'name': 'file_search',
'description': 'Recursively search for files in a subdirectory that match the regex pattern',
'parameters': {
'type': 'object',
'properties': {'dir_path': {
'description': 'Subdirectory to search in.',
'default': '.',
'type': 'string'},
'pattern': {
'description': 'Unix shell regex, where * matches everything.',
'type': 'string'}},
'required': ['pattern']}}
```
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| 1,761,624,274 |
PR_kwDOIPDwls5TPnlC
| 6,323 |
fix link of callbacks on modules page
|
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closed
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[] | null | 2 | 2023-06-17T04:01:13 | 2023-06-18T22:08:13 | 2023-06-18T22:08:12 |
CONTRIBUTOR
| null |
Since [Callbacks](https://python.langchain.com/docs/modules/callbacks/getting_started/) on [Modules](https://python.langchain.com/docs/modules/) went to a "Page Not Found".
|
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| 1,761,594,228 |
I_kwDOIPDwls5o_8t0
| 6,322 |
langchain\agents\chat\output_parser.py line 18, IndexError: list index out of range
|
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[] | null | 5 | 2023-06-17T02:35:41 | 2023-10-09T16:06:41 | 2023-10-09T16:06:40 |
NONE
| null |
### System Info
langchain ver: 0.0.202
python: 3.10_3
I've got this error:
```python
Traceback (most recent call last):
File "C:\Users\catsk\SourceCode\azure_openai_poc\venv\lib\site-packages\langchain\agents\chat\output_parser.py", line 18, in parse
action = text.split("```")[1]
IndexError: list index out of range
```
while setting my agent.type is CHAT_ZERO_SHOT_REACT_DESCRIPTION
At that moment, the text content was:
<details><summary>Details</summary>
<p>
I have found the answer to the question.
Final Answer: Yes, xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.
</p>
</details>
### 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
* Reproduce steps:
1. Build any agent including some tools,
2. set the AgentType as CHAT_ZERO_SHOT_REACT_DESCRIPTION
3. ask the agent doing something
Here is the code:
```python
def local_vector_search(question_str,chat_history, collection_name = hr_collection_name):
embedding = get_openaiembeddings()
vectorstore = Chroma( embedding_function=embedding,
collection_name=collection_name,
persist_directory=root_file_path+persist_db,
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, ai_prefix = "AI超級助理")
llm = AzureOpenAI(
deployment_name = global_deployment_id,
model_name= global_model_name,
temperature = 0.0)
chat_llm = AzureChatOpenAI(
deployment_name = global_deployment_id,
model_name= global_model_name,
temperature = 0.2)
prompt = PromptTemplate(
template=get_prompt_template_string(),
input_variables=["question","chat_history"]
)
prompt.format(question=question_str,chat_history=chat_history)
km_chain = ConversationalRetrievalChain.from_llm(
llm=chat_llm,
retriever=vectorstore.as_retriever(),
memory=memory,
condense_question_prompt=prompt,
)
km_tool = Tool(
name='Knowledge Base',
func=km_chain.run,
description='Use this tool first when you want to answer any issue about our company'
)
math_math = LLMMathChain(llm=llm)
math_tool = Tool(
name='Calculator',
func=math_math.run,
description='Useful for when you need to answer questions about math.'
)
tools=[math_tool,km_tool]
agent=initialize_agent(
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=tools,
llm=chat_llm,
verbose=True,
memory=memory,
max_iterations=30,
)
#result=km_chain(question_str)
result=agent(question_str)
print(result)
return result["output"]
```
### Expected behavior
Give me the final answer
|
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| 1,761,577,931 |
PR_kwDOIPDwls5TPdzX
| 6,321 |
The openai_info.py does not have gpt-35-turbo which is the underlying Azure Open AI model name
|
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closed
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[] | null | 2 | 2023-06-17T01:42:24 | 2023-06-26T09:16:40 | 2023-06-26T09:16:40 |
CONTRIBUTOR
| null |
Since this model name is not there in the list MODEL_COST_PER_1K_TOKENS, when we use get_openai_callback(), for gpt 3.5 model in Azure AI, we do not get the cost of the tokens. This will fix this issue
#### Who can review?
@hwchase17
@agola11
|
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