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Rely on SQLAlchemy to apply parameterized args for each dialect.
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Add _type to json functions output parser
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5b55Aw
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bump 307
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Issue: sqlite3.OperationalError: unable to open database file
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NONE
| null |
### Issue you'd like to raise.
Hi, I have implemented the indexing workflow as in https://python.langchain.com/docs/modules/data_connection/indexing making use of Pinecone and sqlite. This runs perfect within a Pycharm dev environment. But after building the app with Pyinstaller, I get the following error: sqlite3.OperationalError: unable to open database file. I think this one is coming from langchain.indexes.index. The error message is very cryptic. Why can't this file be opened? Is it locked? Is it corrupted - no as I can open it with a desktop sql manager app. I tried different locations for storing the sqlite database but makes no difference.
Does anyone has some ideas on how to solve this?
### Suggestion:
_No response_
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[] | null | 1 | 2023-10-04T10:08:12 | 2023-10-04T14:17:47 | 2023-10-04T14:17:47 |
CONTRIBUTOR
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- **Description:** Adds support for custom API URL in the GitHubIssuesLoader. This allows it to be used with Github enterprise instances.
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Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
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| 1,925,791,540 |
PR_kwDOIPDwls5b4M05
| 11,377 |
a better error description when GCP project is not set
|
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[] | null | 2 | 2023-10-04T09:31:48 | 2023-10-04T15:57:47 | 2023-10-04T15:57:47 |
CONTRIBUTOR
| null |
- **Description:** a little bit better error description
- **Issue:** #10879
|
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I_kwDOIPDwls5yyOYT
| 11,376 |
ConversationBufferMemory returns empty string (and not a list) when empty, breaking agents with memory ("should be a list of base messages, got")
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[] | null | 1 | 2023-10-04T09:18:45 | 2023-10-04T09:21:02 | null |
CONTRIBUTOR
| null |
### System Info
LangChain==0.0.306
Python 3.10.12
(Ubuntu Linux 20.04.6)
### Who can help?
The `ConversationBufferMemory` returns an empty string instead of an empty list when there's nothing stored, which breaks the expectations of the `MessagesPlaceholder` used within the Conversational REACT agent.
Related: https://github.com/langchain-ai/langchain/issues/7365 (where it was commented that changing the loading logic in `ConversationBufferWindowMemory` could break other things).
A possible solution that seems safer (in light of the subsequent type-checking code) could be to default empty strings to empty lists in the `format_messages` method of the `MessagesPlaceholder`, i.e.:
```
class MessagesPlaceholder(BaseMessagePromptTemplate):
...
def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
value = kwargs[self.variable_name]
if not value: ## <-- ADDED CODE
value = [] ## <-- ADDED CODE
if not isinstance(value, list):
raise ValueError(...)
for v in value:
if not isinstance(v, BaseMessage):
raise ValueError(...)
return value
...
```
### 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
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To reproduce the behaviour, make sure you have an OPENAI_API_KEY env var and run the following:
```
# pip install langchain openai
import os
from langchain.memory import ChatMessageHistory, ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
from langchain.tools import BaseTool
conversational_memory = ConversationBufferMemory(
memory_key="chat_history",
chat_memory=ChatMessageHistory()
)
llm = ChatOpenAI(
openai_api_key=os.environ['OPENAI_API_KEY'],
temperature=0,
model_name="gpt-4"
)
class MyTool(BaseTool):
name = "yell_loud"
description = "The tool to yell loud!!!"
def _run(self, query, run_manager=None):
"""Use the tool."""
return 'WAAAAH!'
async def _arun(self, query, run_manager=None):
"""Use the tool asynchronously."""
raise NotImplementedError("no async")
agent = initialize_agent(
agent="chat-conversational-react-description",
tools=[MyTool()],
llm=llm,
max_iterations=5,
verbose=True,
memory=conversational_memory,
early_stopping_method='generate'
)
print(agent.run("Please yell very loud, thank you, and then report the result to me."))
# Will raise:
# ValueError: variable chat_history should be a list of base messages, got
# at location:
# "langchain/prompts/chat.py", line 98, in format_messages
```
### Expected behavior
I would expect the script to not complain when the memory is empty and result in something like the following agent interaction (as I got from the modified `format_messages` tentatively suggested above):
```
$> python agent_script.py
> Entering new AgentExecutor chain...
'''json
{
"action": "yell_loud",
"action_input": "Please yell very loud, thank you"
}
'''
Observation: WAAAAH!
Thought:'''json
{
"action": "Final Answer",
"action_input": "The response to your last comment was a loud yell, as you requested."
}
'''
> Finished chain.
The response to your last comment was a loud yell, as you requested.
```
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| 1,925,720,401 |
I_kwDOIPDwls5yyClR
| 11,375 |
How to change the system message of qa chain?
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open
| false | null |
[] | null | 1 | 2023-10-04T08:53:33 | 2023-10-04T09:03:54 | null |
NONE
| null |
### Issue you'd like to raise.
I have applied RetrievalQA's chain_type to map_reduce in my application and would like to customize its system message.
### Suggestion:
There is a way to modify the map_reduce_prompt code in the question answering, but I would like to know how to change it to the argument value of RetrievalQA.from_chain_type.
|
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| 1,925,542,037 |
PR_kwDOIPDwls5b3XMZ
| 11,374 |
Fix ConversationSummaryBufferMemory to flush old interactions with th…
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| false | null |
[] | null | 2 | 2023-10-04T07:05:33 | 2023-10-09T00:51:21 | null |
CONTRIBUTOR
| null |
…e maximum tokens of the buffer
- **Issue:** #3072
- **Relate to:** #3345
- **Description:** I've found same issue in my project with langchain and also read the related PR.
However, I thought that instead of actually pruning the messages, just getting the small size buffer is more appropriate.
So, this is what I've done.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,925,448,887 |
I_kwDOIPDwls5yxAS3
| 11,373 |
Issue: Open AI embedding dose not returning results for some times
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[] | null | 2 | 2023-10-04T05:46:24 | 2023-10-04T19:54:57 | null |
NONE
| null |
### Issue you'd like to raise.
I am having python REST API which calls to Open AI using following code for embedding.
`openai.Embedding.create(input=text, engine='text-embedding-ada-002')['data'][0]['embedding']`
I am having text length below the 8191 (max token size for text-embedding-ada-002).
When I am calling it, some times it doesn't return any results or exceptions. (execution get blocks after the calling this. But it is giving result on 2nd or 3rd attempts for same input text. It is happening time to time and not consistence.
Kindly help me to resolve this issue.
Thanks
Nuwan
### Suggestion:
_No response_
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rupd
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[] | null | 1 | 2023-10-04T04:08:23 | 2023-11-15T23:33:31 | 2023-11-15T23:33:31 |
COLLABORATOR
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
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If you're adding a new integration, please include:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls5ywmHc
| 11,371 |
RAG - TypeError: unhashable type: 'list'
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[] | null | 5 | 2023-10-04T03:33:06 | 2023-10-04T15:35:46 | 2023-10-04T15:14:32 |
NONE
| null |
### System Info
I running code in Google Colab:
LangChain version: 0.0.306
Python 3.10.12
Transformers library 4.34.0
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hello! I have encountered the following error when trying to develop HuggingFace question answering models:
tokenizer = AutoTokenizer.from_pretrained("/content/flan-t5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("/content/flan-t5-large")
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
llm = HuggingFacePipeline(
pipeline = pipeline,
model_kwargs={"temperature": 0, "max_length": 512},
)
chain = load_qa_chain(llm, chain_type="stuff")
query = "My question?"
docs = db.similarity_search(query)
chain.run(input_documents=docs, question=query)
But the output is:
TypeError Traceback (most recent call last)
[<ipython-input-34-226234bd6dea>](https://localhost:8080/#) in <cell line: 1>()
----> 1 chain.run(input_documents=docs, question=query)
15 frames
[/usr/local/lib/python3.10/dist-packages/transformers/pipelines/__init__.py](https://localhost:8080/#) in pipeline(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)
773
774 # Retrieve the task
--> 775 if task in custom_tasks:
776 normalized_task = task
777 targeted_task, task_options = clean_custom_task(custom_tasks[task])
TypeError: unhashable type: 'list'
### Expected behavior
I just wanna find solution, I didn't find nothing about this in the Google.
|
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I_kwDOIPDwls5ywkOJ
| 11,370 |
Issue: Limit Memory Access on LangChain Bot
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open
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[] | null | 1 | 2023-10-04T03:27:22 | 2023-10-04T03:35:38 | null |
NONE
| null |
### Issue you'd like to raise.
I'm creating a langchain chatbot (Conversationl-ReACT-description) with zep long term memory and that bot has access to real-time data. How do I prevent use the memory when same query asked again few minutes later?
### Suggestion:
_No response_
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PR_kwDOIPDwls5b14_3
| 11,369 |
send single batch of generations
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5b1vT2
| 11,368 |
Wfh/eval max concurrency
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COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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add default for async
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Updates to Vectara Implementation
|
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[] | null | 1 | 2023-10-03T22:07:35 | 2023-10-03T22:34:39 | 2023-10-03T22:34:39 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** updates to documentation and API headers
- **Tag maintainer:** @baskarya
- **Twitter handle:** @ofermend
|
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| 1,924,996,244 |
I_kwDOIPDwls5yvRyU
| 11,365 |
Issue: differences between tool and llm
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[] | null | 1 | 2023-10-03T21:42:07 | 2023-10-04T01:55:45 | null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
Hi, I want to clarify some questions about tool and llm usage.
If an agent is initialized with multiple llm and tools,
1. how does the agent choose which llm or tool to use to answer the prompt?
2. How is llm different from tools? Can you give some examples of each category?
3. Can the agent use multiple tools/llm? If the agent can use multiple tools, what happen if the output generated by such tools/llm conflict with each other? In that case, which result will be returned to user?
for example, in the code beloew (cited from langchain doc), the agent is initialized with both 'llm_chain=llm_chain' (where llm_chain is OpenAI)and 'tools=tools' (where tool is google search engine). Which one is used to generate the result?
```search = GoogleSearchAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
)
]
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
memory = ConversationBufferMemory(memory_key="chat_history")
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, memory=memory
)
agent_chain.run(input="How many people live in canada?")
agent_chain.run(input="what is their national anthem called?")```
|
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| 1,924,944,707 |
PR_kwDOIPDwls5b1Ww4
| 11,364 |
chore: add kotlin code splitter
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** Adds Kotlin language to `TextSplitter`
|
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| 1,924,939,090 |
I_kwDOIPDwls5yvD1S
| 11,363 |
Does langchain index the speaker notes in powerpoint?
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[] | null | 5 | 2023-10-03T21:00:18 | 2023-10-04T20:53:13 | null |
NONE
| null |
### Issue you'd like to raise.
When I use directory loader, does langchain index the speaker notes in powerpoint?
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5b1VHX
| 11,362 |
add getattr case for llms.type_to_cls_dict
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[] | null | 1 | 2023-10-03T20:58:57 | 2023-10-03T21:34:32 | 2023-10-03T21:34:31 |
COLLABORATOR
| null |
For external libraries that depend on `type_to_cls_dict`, adds a workaround to continue using the old format.
Recommend people use `get_type_to_cls_dict()` instead and only resolve the imports when they're used.
|
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PR_kwDOIPDwls5b1KpT
| 11,361 |
Solves unicode [codec can't decode byte] traceback error.
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NONE
| null |
Following up on #11359
- **Description:** a description of the change,
privateGPT/ingest.py crashes when parsing a txt file with unexpected unicode.
- **Issue:** the issue # it fixes (if applicable),
Problem solved:
Traceback (most recent call last):
File "C:\Program Files\Python3\Lib\site-packages\langchain\document_loaders\text.py", line 41, in load text = f.read()
^^^^^^^^
File "", line 322, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa7 in position 549: invalid start byte
- **Dependencies:** any dependencies required for this change,
none
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
No idea.
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
@DigitalVisor
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PR_kwDOIPDwls5b1HHo
| 11,360 |
added interactive login for azure cognitive search vector store
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[] | null | 3 | 2023-10-03T20:14:32 | 2023-10-05T23:01:51 | 2023-10-05T16:20:18 |
CONTRIBUTOR
| null |
**Description:** Previously if the access to Azure Cognitive Search was not done via an API key, the default credential was called which doesn't allow to use an interactive login. I simply added the option to use "INTERACTIVE" as a key name, and this will launch a login window upon initialisation of the AzureSearch object.
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I_kwDOIPDwls5yup1O
| 11,359 |
Issue: Fixed unicode decode byte error
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[] | null | 2 | 2023-10-03T19:47:13 | 2023-10-03T20:31:47 | null |
NONE
| null |
### Issue you'd like to raise.
Trying to submit a code update. This would be a first. Help a non software eng out. :)
Here's a manual diff of the intended change:
```
langchain\libs\langchain\langchain\document_loaders\text.py
<<[41] with open(self.file_path, encoding=self.encoding) as f:
>>[41] with open(self.file_path, encoding=self.encoding, errors='replace') as f:
```
The COMMIT_EDITMSG file:
"""
Solves unicode [codec can't decode byte] traceback error:
Traceback (most recent call last):
File "C:\Program Files\Python3\Lib\site-packages\langchain\document_loaders\text.py", line 41, in load
text = f.read()
^^^^^^^^
File "<frozen codecs>", line 322, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xa7 in position 549: invalid start byte
"""
The general flow used:
PS C:\dev\github\langchain\libs\langchain\langchain\document_loaders> notepad .\text.py
PS C:\dev\github\langchain\libs\langchain\langchain\document_loaders> git add text.py
PS C:\dev\github\langchain\libs\langchain\langchain\document_loaders> git checkout -b unicode_replace_fix
Switched to a new branch 'unicode_replace_fix'
PS C:\dev\github\langchain\libs\langchain\langchain\document_loaders> git commit -m "Solves unicode [codec can't decode byte] traceback error."
... // had to update user.name/email
PS C:\dev\github\langchain\libs\langchain\langchain\document_loaders> git commit --amend --reset-author
hint: Waiting for your editor to close the file... unix2dos: converting file C:/Dev/Github/langchain/.git/COMMIT_EDITMSG to DOS format...
dos2unix: converting file C:/Dev/Github/langchain/.git/COMMIT_EDITMSG to Unix format...
[unicode_replace_fix 09c9cb77e] Solves unicode [codec can't decode byte] traceback error:
1 file changed, 1 insertion(+), 1 deletion(-)
// below, I got a github auth popup, went through, succeeded - then denied?
PS C:\dev\github\langchain\libs\langchain\langchain\document_loaders> git push origin unicode_replace_fix
info: please complete authentication in your browser...
remote: Permission to langchain-ai/langchain.git denied to hackedpassword.
fatal: unable to access 'https://github.com/langchain-ai/langchain/': The requested URL returned error: 403
// denied but no diff?
PS C:\dev\github\langchain> git diff HEAD
PS C:\dev\github\langchain> git status
On branch unicode_replace_fix
nothing to commit, working tree clean
PS C:\dev\github\langchain> git diff
PS C:\dev\github\langchain>
Maybe I'm up against a repo push permission issue?
### Suggestion:
_No response_
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BedrockChat Error
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[] | null | 1 | 2023-10-03T19:43:11 | 2023-10-03T19:49:32 | null |
NONE
| null |
### Feature request
Chatbot using prompt template (Langchain) gives proper response to first prompt "Hi there!" but gives below error to second prompt "Give me a few tips on how to start a new garden."
_```
ValueError: Error: Prompt must alternate between '
Human:' and '
Assistant:'.
### Motivation
Yes its a problem being raised by my customer who is blocked currently
### Your contribution
NA
[Error-Test.ipynb.txt](https://github.com/langchain-ai/langchain/files/12796468/Error-Test.ipynb.txt)
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| 11,357 |
Create ChatEverlyAI
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CONTRIBUTOR
| null |
- Description: Adds the ChatEverlyAI class with llama-2 7b on [EverlyAI Hosted
Endpoints](https://everlyai.xyz/)
- It inherits from ChatOpenAI and requires openai (probably unnecessary
but it made for a quick and easy implementation)
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PR_kwDOIPDwls5b0ixz
| 11,356 |
Use parameterized queries to select SQL schemas.
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[] | null | 2 | 2023-10-03T18:33:59 | 2023-10-04T04:43:31 | 2023-10-04T04:43:30 |
COLLABORATOR
| null |
This switches to parameterized queries for selecting schemas for all databases, except DuckDB for which I wasn't able to figure out how to get parameterized queries to work: https://github.com/Mause/duckdb_engine/issues/796
For all the other databases, I followed the parameterized query approach described by their docs.
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PR_kwDOIPDwls5b0gBe
| 11,355 |
Tweak type hints to match dependency's behavior.
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[] | null | 1 | 2023-10-03T18:25:31 | 2023-10-05T02:36:59 | 2023-10-05T02:36:58 |
COLLABORATOR
| null |
Needs #11353 to merge first, and a new `langchain` to be published with those changes.
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PR_kwDOIPDwls5b0c9e
| 11,353 |
Improve type hints and interface for SQL execution functionality.
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[] | null | 1 | 2023-10-03T18:16:34 | 2023-10-03T19:19:10 | 2023-10-03T19:19:09 |
COLLABORATOR
| null |
The previous API of the `_execute()` function had a few rough edges that this PR addresses:
- The `fetch` argument was type-hinted as being able to take any string, but any string other than `"all"` or `"one"` would `raise ValueError`. The new type hints explicitly declare that only those values are supported.
- The return type was type-hinted as `Sequence` but using `fetch = "one"` would actually return a single result item. This was incorrectly suppressed using `# type: ignore`. We now always return a list.
- Using `fetch = "one"` would return a single item if data was found, or an empty *list* if no data was found. This was confusing, and we now always return a list to simplify.
- The return type was `Sequence[Any]` which was a bit difficult to use since it wasn't clear what one could do with the returned rows. I'm making the new type `Dict[str, Any]` that corresponds to the column names and their values in the query.
I've updated the use of this method elsewhere in the file to match the new behavior.
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PR_kwDOIPDwls5b0WFr
| 11,350 |
Use an enum for configurable_alternatives to make the generated json schema nicer
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[] | null | 1 | 2023-10-03T17:54:22 | 2023-10-04T15:32:43 | 2023-10-04T15:32:42 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5b0AzP
| 11,349 |
Make a copy of external data instead of mutating another object's attributes.
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[] | null | 1 | 2023-10-03T16:50:51 | 2023-10-03T19:27:53 | 2023-10-03T19:27:52 |
COLLABORATOR
| null |
Fix for a bug surfaced as part of #11339. `mypy` caught this since the types didn't match up.
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PR_kwDOIPDwls5bz7Co
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Add amazon_bedrock_chat to get_type_to_cls_dict
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NONE
| null |
**Description:** Add `BedrockChat` to `get_type_to_cls_dict` map
|
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| 1,924,512,627 |
I_kwDOIPDwls5ytbtz
| 11,347 |
Conversational Chain Error 429
|
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[] | null | 3 | 2023-10-03T16:28:32 | 2023-10-04T02:01:02 | null |
NONE
| null |
### System Info
Today, all of a sudden I'm getting a error status code 429 using the Conversation chain. Nothing has changed, expect the day I've run the script. Any ideas?
It works with the normal LLM chain, so it must be to do with the Conversation Chain not working or the memories.
Using Flowise.
Chrome
@hwchase17
@agola11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
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### Reproduction
Request failed with status code 429 and body {"error":{"message":"Rate limit reached for 10KTPM-200RPM in organization org-ByeURhCRRujcKf7u7QrlrudH on tokens per min. Limit: 10000 / min. Please try again in 6ms. Contact us through our help center at help.openai.com if you continue to have issues.","type":"tokens","param":null,"code":"rate_limit_exceeded"}}
### Expected behavior
I expect the LLM chain to write the content.
|
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| 1,924,454,276 |
PR_kwDOIPDwls5bzsK_
| 11,346 |
fix get_tool_return
|
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| 1,924,378,495 |
PR_kwDOIPDwls5bzb2d
| 11,344 |
Upgrade CI workflows to poetry 1.6.1.
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PR_kwDOIPDwls5bzarW
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Upgrade root poetry dependencies and upgrade to poetry 1.6.1.
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PR_kwDOIPDwls5bzXvu
| 11,342 |
better error message on parsing errors
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I_kwDOIPDwls5ys1EU
| 11,341 |
Missing 1 required positional argument for function that has `self` argument.
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NONE
| null |
### System Info
langchain==0.0.286
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
``` python
def add_product(self, input: str):
print(f"🟢 Add product {input}")
```
``` python
tools = [
Tool(
name="AddProduct",
func=add_product,
description="""
Add product to order.
Input of this tool must be a single string JSON format:
For example: '{{"name":"Nike Pegasus 40"}}'
"""
)
]
```
``` python
agent = initialize_agent(
tools=tools,
llm=__gpt4,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True,
prompt=prompt
)
```
### Expected behavior
Error:
```
TypeError: add_product() missing 1 required positional argument: 'input'
```
Expected:
```
Function `add_product` called with correct input.
```
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PR_kwDOIPDwls5bzWDO
| 11,340 |
Fix typo in agent_structured.ipynb
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[] | null | 1 | 2023-10-03T15:04:44 | 2023-10-03T16:00:38 | 2023-10-03T16:00:38 |
CONTRIBUTOR
| null |
therefor -> therefore
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5bzVtM
| 11,339 |
Upgrade experimental package dependencies and use Poetry 1.6.1.
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[] | null | 1 | 2023-10-03T15:03:53 | 2023-10-17T01:13:32 | 2023-10-17T01:13:31 |
COLLABORATOR
| null |
Part of upgrading our CI to use Poetry 1.6.1.
|
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PR_kwDOIPDwls5bzTav
| 11,338 |
Use term keyword according to the official python doc glossary
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[] | null | 1 | 2023-10-03T14:58:40 | 2023-10-03T19:56:09 | 2023-10-03T19:56:09 |
CONTRIBUTOR
| null |
- **Description:** use term keyword according to the official python doc glossary, see https://docs.python.org/3/glossary.html
- **Issue:** not applicable
- **Dependencies:** not applicable
- **Tag maintainer:** @hwchase17
- **Twitter handle:** vreyespue
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I_kwDOIPDwls5ysoB1
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OperationalError: (pyodbc.OperationalError) ('08001', '[08001]
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[] | null | 24 | 2023-10-03T14:39:18 | 2023-10-12T09:21:33 | null |
NONE
| null |
### System Info
latest version of langchain. python=3.11.4
### Who can help?
@all
### Information
- [x] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents import *
from langchain.llms import OpenAI
from langchain.sql_database import SQLDatabase
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain.agents import AgentExecutor
from langchain.chat_models import ChatOpenAI
# from secret_key import openapi_key
openapi_key = "######"
os.environ['OPENAI_API_KEY'] = openapi_key
def chat(question):
llm = OpenAI(temperature=0)
tools = load_tools(["llm-math"], llm=llm)
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
driver = 'ODBC Driver 17 for SQL Server'
host = '####'
database = 'chatgpt'
user = 'rnd'
password = '###'
#db_uri = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver={driver}")
db = SQLDatabase.from_uri(f"mssql+pyodbc://{user}:{password}@{host}/{database}?driver=ODBC+Driver+17+for+SQL+Server")
llm = ChatOpenAI(model_name="gpt-3.5-turbo",
temperature=0,
max_tokens=1000)
# toolkit = SQLDatabaseToolkit(db=db)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
reduce_k_below_max_tokens=True,
)
mrkl = initialize_agent(
tools,
ChatOpenAI(temperature=0),
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
)
return agent_executor.run(question)
### Expected behavior
i need to connect to test server, but i'm getting an error while connecting to it, but is working fine on local server , been getting
"OperationalError: (pyodbc.OperationalError) ('08001', '[08001] [Microsoft][ODBC Driver 17 for SQL Server]Named Pipes Provider: Could not open a connection to SQL Server [53]. (53) (SQLDriverConnect); [08001] [Microsoft][ODBC Driver 17 for SQL Server]Login timeout expired (0); [08001] [Microsoft][ODBC Driver 17 for SQL Server]A network-related or instance-specific error has occurred while establishing a connection to SQL Server. Server is not found or not accessible. Check if instance name is correct and if SQL Server is configured to allow remote connections. For more information see SQL Server Books Online. (53)')
"
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PR_kwDOIPDwls5bzCI0
| 11,336 |
update sec doc
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[] | null | 1 | 2023-10-03T14:21:03 | 2023-10-03T14:22:54 | 2023-10-03T14:22:53 |
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PR_kwDOIPDwls5by2OV
| 11,335 |
Add explanations to GoogleDriveLoader how to avoid errors
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CONTRIBUTOR
| null |
- **Description:** add a paragraph to the GoogleDriveLoader doc on how to bypass errors on authentication.
For some reason, specifying credential path via `credentials_path` constructor parameter when creating `GoogleDriveLoader` makes it so that the oAuth screen is never showing up when first using GoogleDriveLoader. Instead, the `RefreshError: ('invalid_grant: Bad Request', {'error': 'invalid_grant', 'error_description': 'Bad Request'})` error happens. Setting it via `os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = ...` solves the problem. Also, `token_path` constructor parameter is mandatory, otherwise another error happens when trying to `load()` for the first time.
These errors are tricky and time-consuming to figure out, so I believe it's good to mention them in the docs.
|
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Async support for FAISS
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CONTRIBUTOR
| null |
Following this tutoral about using OpenAI Embeddings with FAISS
https://python.langchain.com/docs/integrations/vectorstores/faiss
```python
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.document_loaders import TextLoader
loader = TextLoader("../../../extras/modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
```
This works fine
```python
db = FAISS.from_documents(docs, embeddings)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
```
But the async version is not
```python
db = await FAISS.afrom_documents(docs, embeddings) # NotImplementedError
query = "What did the president say about Ketanji Brown Jackson"
docs = await db.asimilarity_search(query) # this will use await asyncio.get_event_loop().run_in_executor under the hood and will not call OpenAIEmbeddings.aembed_query but call OpenAIEmbeddings.embed_query
```
So this PR add async/await supports for FAISS
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In ProgressBarCallback update the progress counter also when runs fin…
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[] | null | 1 | 2023-10-03T12:31:30 | 2023-10-04T04:05:00 | 2023-10-04T04:04:59 |
COLLABORATOR
| null |
…ish with an error
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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I_kwDOIPDwls5yrZ_J
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Issue: Documents embeddings with many and similar numbers don't return good results
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[] | null | 4 | 2023-10-03T12:04:42 | 2023-10-20T19:45:14 | null |
NONE
| null |
### Issue you'd like to raise.
Hi! I am trying to create a question answering chatbot with PDFs. There is something different in these documents. I have different articles with their number, example: article 1.2.2.1.3, article 1.2.3.4.5, article 2.3.4.1.3, etc.
When I ask for a specific article, it can't find the answer and return "the article x.x.x.x.x is not in the context". I have tried with some embeddings techniques and vector store, but it does not work.
Any ideas?
PD: The PDF documents are around 450 pages
### Suggestion:
_No response_
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| 11,329 |
Add Vespa vector store
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[] | null | 2 | 2023-10-03T11:49:15 | 2023-10-04T18:59:11 | 2023-10-04T18:59:11 |
CONTRIBUTOR
| null |
Addition of Vespa vector store integration including notebook showing its use.
Maintainer: @lesters
Twitter handle: LesterSolbakken
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| 1,923,856,712 |
PR_kwDOIPDwls5bxqkw
| 11,328 |
Add client_name="langchain" to Cohere usage
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[] | null | 2 | 2023-10-03T10:48:42 | 2023-10-30T18:20:55 | 2023-10-30T18:20:55 |
CONTRIBUTOR
| null |
Hey, we're looking to invest more in adding cohere integrations to langchain so would love to get more of an idea for how it's used. Hopefully this pr is acceptable. This week I'm also going to be looking into adding our new [retrieval augmented generation product](https://txt.cohere.com/chat-with-rag/) to langchain.
|
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PR_kwDOIPDwls5bxfG9
| 11,327 |
Added autodetect_encoding option to csvLoader
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CONTRIBUTOR
| null |
Added autodetect_encoding option to csvLoader like TextLoader.
|
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PR_kwDOIPDwls5bxJxS
| 11,326 |
Add HTML Title and Page Language into metadata for AsyncHtmlLoader
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[] | null | 2 | 2023-10-03T09:23:35 | 2023-10-27T03:22:31 | 2023-10-27T03:22:31 |
CONTRIBUTOR
| null |
**Description:**
Revise `libs/langchain/langchain/document_loaders/async_html.py` to store the HTML Title and Page Language in the `metadata` of `AsyncHtmlLoader`.
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I_kwDOIPDwls5yqUFu
| 11,325 |
Issue: LLMChain arun running with Llama7B encounter NotImplementError
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[] | null | 1 | 2023-10-03T09:18:23 | 2023-10-03T09:26:01 | null |
NONE
| null |
### Issue you'd like to raise.
Yeah, so I've been attempting to load Llama2 and utilize Langchain to create a condensed model for my documents. I managed to accomplish the task successfully, as Llama2 generated the results quite effectively. However, when I tried to speed up the process by referencing Langchain's [LLMChain ](https://api.python.langchain.com/en/latest/chains/langchain.chains.llm.LLMChain.html?highlight=llmchain#langchain.chains.llm.LLMChain)and one of its function -- arun, I kept encountering an error that said "Not Implement Error". It didn't matter which function I tried to use, whether it was acall, arun, or apredict, they all failed.
So here are my codes:
```
# load LLama from local drive
tokenizer=AutoTokenizer.from_pretrained("/content/drive/MyDrive/CoLab/LLama7b")
model=LlamaForCausalLM.from_pretrained("/content/drive/MyDrive/CoLab/LLama7b",quantization_config=quant_config,device_map="auto",)
# define output parameters
prefix_llm=transformers.pipeline(
model=model, tokenizer=tokenizer,
return_full_text=True,
task="text-generation",# temperature=0,
top_p=0.15, top_k=15,
max_new_tokens=1060, repetition_penalty=1.2,
do_sample=True
)
# wrap up with Langchain HuggingfaceLLm class
llm=HuggingFacePipeline(pipeline=prefix_llm, cache=False)
# Combine with LLMChain
prompt: PromptTemplate, input_variables="words"
llm_chain = LLMChain(prompt=prompt, llm=llm)
# calling arun
input: list[str]
llm_chain.run(input[num]) || llm_chain.run(words=input[num]) # run smoothly
llm_chain.arun(input) || llm_chain.arun(words=input) # Raise error NotImplementError
```
```
error comes from langchain/llms/base.py in _agenerate(self, prompts, stop, run_manager, **kwargs)
479 ) -> LLMResult:
480 """Run the LLM on the given prompts."""
--> 481 raise NotImplementedError()
482
483 def _stream(
NotImplementedError:
# in this last function, "run_manager" should be None, "stop" too.
```
As far as I explored, this process will call 10 frames. Start on
```
langchian/chains/base.py -> def acall() to
langchian/chains/base.py -> def agenerate()
then jump to
langchian/chains/llm.py -> def _acall() to
langchian/chains/llm.py -> def agenerate()
in the last it stopped at
langchian/llms/base.py -> def agenerate() to
langchian/llms/base.py -> def agenerate_prompt(),
langchian/llms/base.py -> def agenerate_helper()
langchian/llms/base.py -> def _agenerate()
```
I don't know what "thing" I should implement or Langchian didn't implement. the calling code is 100% refer to the [Langchain example](https://python.langchain.com/docs/modules/chains/how_to/async_chain). I suspect maybe it's because Langchain didn't fully support Llama? Since it runs smoothly on OpenAI API.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bw870
| 11,324 |
Pass kwargs in runnable retry
|
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[] | null | 1 | 2023-10-03T08:51:22 | 2023-10-03T08:55:04 | 2023-10-03T08:55:03 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5bwaq4
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[Bug] fix when data[key] is False
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PR_kwDOIPDwls5bwL2H
| 11,322 |
Fixed exception type in py files
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[] | null | 1 | 2023-10-03T06:25:09 | 2023-10-04T01:46:26 | 2023-10-04T01:46:26 |
CONTRIBUTOR
| null |
I've refactored the code to ensure that ImportError is consistently handled. Instead of using ValueError as before, I've now followed the standard practice of raising ImportError along with clear and informative error messages. This change enhances the code's clarity and explicitly signifies that any problems are associated with module imports.
CC: @baskaryan , @hwchase17
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update schema
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I_kwDOIPDwls5yozek
| 11,319 |
CodeInterpreter for Langchain
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NONE
| null |
### Issue you'd like to raise.
Is there a codeinterpreter plugin for langchain?
Use Case: I want to generate insights with charts as like the paid ChatGPT out there. Is this possible?
### Suggestion:
_No response_
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PR_kwDOIPDwls5bvsDf
| 11,318 |
Update clarifai.mdx
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[] | null | 1 | 2023-10-03T04:10:00 | 2023-10-03T05:16:51 | 2023-10-03T05:16:00 |
CONTRIBUTOR
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@baskaryan , Small typo fix
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PR_kwDOIPDwls5bvq90
| 11,317 |
Fixed typo errors
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CONTRIBUTOR
| null |
@baskaryan, Fixed a small typo errors
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Change warning class for deprecations used in init
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[] | null | 2 | 2023-10-03T01:18:02 | 2023-12-02T02:37:34 | 2023-12-02T02:37:34 |
COLLABORATOR
| null |
Change warning class to LangChain Deprecation Warning instead of generic User Warning
|
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Add prompt hub support for Mistral w/ Ollama
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[] | null | 1 | 2023-10-03T00:06:54 | 2023-10-03T15:17:48 | 2023-10-03T15:17:47 |
COLLABORATOR
| null |
Add Mistral example with prompt support
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PR_kwDOIPDwls5but7C
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Adds Tavily Search API retriever
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[] | null | 1 | 2023-10-02T23:21:30 | 2023-10-03T00:12:18 | 2023-10-03T00:12:17 |
CONTRIBUTOR
| null |
@baskaryan @efriis
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I_kwDOIPDwls5ymz9J
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Azure Cognitive Search vector DB store performs slow embedding as it does not utilize the batch embedding functionality
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[] | null | 4 | 2023-10-02T21:42:29 | 2023-11-06T11:52:03 | null |
NONE
| null |
### System Info
- Langchain Version: 0.0.306
- Python Version: 3.10.11
- Azure Cognitive Seach (any sku)
- Embedding that has batch functionality
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Get a document (or set of documents) that will produce a few hundred chunks
2. Run the sample code provided by Langchain to index those documents into the Azure Search vector store (sample code below)
3. Run steps 1 and 2 on another vector store that supports batch embedding (milvus implementation supports batch embeddings)
4. Analyze the delta between the two vectorization speeds (Azure Search should be noticeably slower)
Sample code:
```python
import openai
import os
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores.azuresearch import AzureSearch
vector_store_address: str = "YOUR_AZURE_SEARCH_ENDPOINT"
vector_store_password: str = "YOUR_AZURE_SEARCH_ADMIN_KEY"
embeddings: OpenAIEmbeddings = OpenAIEmbeddings(deployment=model, chunk_size=1)
index_name: str = "langchain-vector-demo"
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query,
)
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
loader = TextLoader("../../../state_of_the_union.txt", encoding="utf-8")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
vector_store.add_documents(documents=docs)
```
### Expected behavior
The expected behavior for the Azure search implementation to also support batch embedding if the implementation of the embedding class supports batch embedding. This should result in significant speed improvements when comparing the single embedding approach vs the batch embedding approach.
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PR_kwDOIPDwls5bt8s8
| 11,312 |
merge pages into `google` and `AWS` pages
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COLLABORATOR
| null |
There are several pages in `integrations/providers/more` that belongs to Google and AWS `integrations/providers`.
- moved content of these pages into the Google and AWS `integrations/providers` pages
- removed these individual pages
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PR_kwDOIPDwls5bt1Wc
| 11,311 |
Deprecate direct access to globals like `debug` and `verbose`.
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[] | null | 4 | 2023-10-02T21:15:54 | 2023-10-17T04:02:36 | 2023-10-12T22:48:04 |
COLLABORATOR
| null |
Instead of accessing `langchain.debug`, `langchain.verbose`, or `langchain.llm_cache`, please use the new getter/setter functions in `langchain.globals`:
- `langchain.globals.set_debug()` and `langchain.globals.get_debug()`
- `langchain.globals.set_verbose()` and `langchain.globals.get_verbose()`
- `langchain.globals.set_llm_cache()` and `langchain.globals.get_llm_cache()`
Using the old globals directly will now raise a warning.
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PR_kwDOIPDwls5btuir
| 11,310 |
Fix Fake LLM tool usage example
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[] | null | 2 | 2023-10-02T20:58:02 | 2023-10-08T12:54:05 | 2023-10-08T12:54:05 |
CONTRIBUTOR
| null |
**Description:**
Mock action in a FakeListLLM uses a wrong name for the REPL tool (the correct one uses '_').
Without it, an error pops up: "Python REPL is not a valid tool, try one of [Python_REPL]."
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PR_kwDOIPDwls5bth9p
| 11,309 |
Added custom retry decorator support in ChatOpenAI
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NONE
| null |
Description:
This PR adds the ability for users to provide a custom retry decorator when creating an instance of the ChatOpenAI class. The custom decorator will be used in the acompletion_with_retry and completion_with_retry methods to handle retries for the API calls. If a custom decorator is not provided, the default retry decorator will be used. This feature provides users the flexibility to handle specific use cases and errors in specific ways.
Issue: https://github.com/langchain-ai/langchain/issues/3109
Dependencies: No new dependencies are required for this change.
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PR_kwDOIPDwls5btc-v
| 11,308 |
Add optional arg that provides fix for #11261 (Anthropic w/ BedrockChat)
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[] | null | 3 | 2023-10-02T20:18:38 | 2023-12-03T01:40:38 | null |
NONE
| null |
Fixes #11261
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PR_kwDOIPDwls5btUQh
| 11,307 |
Upgrade `langchain` dependency versions to resolve dependabot alerts.
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I_kwDOIPDwls5ymBLY
| 11,306 |
Upgrade to v :0.0.306 - AttributeError: Could not find: llms
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[] | null | 1 | 2023-10-02T19:50:11 | 2023-10-03T02:58:02 | null |
NONE
| null |
### System Info
v: 0.0306
python version: Python 3.11.4
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Recently upgraded to version 0.0.306 of langchainversion. (from 0.0.259)
I see to run into the below error
```
2023-10-02T19:42:20.279102248Z File "/home/appuser/.local/lib/python3.11/site-packages/langchain/__init__.py", line 322, in __getattr__
2023-10-02T19:42:20.279258947Z raise AttributeError(f"Could not find: {name}")
2023-10-02T19:42:20.279296147Z AttributeError: Could not find: llms
```
I have no idea as to what is causing this and from where the call is being made. I know that the error is coming from the below file (line 328)
https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/__init__.py
I am not able find any attribute called "llms" that is being set in any of our code base - hence more confused about the Attribute error.
### Expected behavior
- Perhaps a stack trace that shows the flow of calls
- Documentation that shows the possibilities of the error
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PR_kwDOIPDwls5btKuI
| 11,305 |
add LLMBashChain to experimental
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[] | null | 2 | 2023-10-02T19:35:28 | 2023-10-02T20:00:15 | 2023-10-02T20:00:14 |
COLLABORATOR
| null |
Add LLMBashChain to experimental
|
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| 1,922,495,596 |
PR_kwDOIPDwls5btApq
| 11,304 |
add youdotcom retriever skeleton
|
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[] | null | 2 | 2023-10-02T19:10:53 | 2023-10-05T21:36:32 | 2023-10-05T20:48:11 |
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** Add you.com retriever to langchain,
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Tag maintainer:** @hwchase17 ,
- **Twitter handle:** TBD!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
|
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| 1,922,487,402 |
I_kwDOIPDwls5yltRq
| 11,303 |
Issue: Abstract Class Implementation problem in Retrievers
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[] | null | 21 | 2023-10-02T19:06:33 | 2023-11-15T12:51:45 | 2023-10-06T09:38:26 |
NONE
| null |
### Issue you'd like to raise.
Hi everyone,
I've encountered an issue while trying to instantiate the ConversationalRetrievalChain in the Langchain library. It seems to be related to the abstract class BaseRetriever and the required method _get_relevant_documents.
Here's my base code:
```
import os
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings.openai import OpenAIEmbeddings
from dotenv import load_dotenv
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
# Load environment variables
load_dotenv("info.env")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# Initialize the FAISS vector database
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
index_filenames = ["index1", "index2"]
dbs = {f"category{i+1}": FAISS.load_local(filename, embeddings) for i, filename in enumerate(index_filenames)}
# Load chat model and question answering chain
llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, openai_api_key=OPENAI_API_KEY)
chain = load_qa_chain(llm, chain_type="map_reduce")
# Initialize conversation memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
# Set the role of the AI
role = "Assistant"
# Write the role of the AI
memory.save_context({"input": f"AI: As an {role}, I can help you with your questions based on the retrievers and my own data."}, {"output": ""})
# Initialize the ConversationalRetrievalChain with memory and custom retrievers
qa = ConversationalRetrievalChain.from_llm(llm, retriever=dbs, memory=memory)
# Pass in chat history using the memory
while True:
user_input = input("User: ")
# Add user input to the conversation history
memory.save_context({"input": f"User: {user_input}"}, {"output": ""})
# Check if the user wants to exit
if user_input == "!exit":
break
# Run the chain on the user's query
response = qa.run(user_input)
# Print the response with a one-line space and role indication
print("\n" + f"{role}:\n{response}" + "\n")
```
And here's the error message I received:
```
pydantic.v1.error_wrappers.ValidationError: 1 validation error for ConversationalRetrievalChain
retriever
Can't instantiate abstract class BaseRetriever with abstract method _get_relevant_documents (type=type_error)
```
It appears that the retriever I provided doesn't fully implement the required methods. Could someone provide guidance on how to properly implement a retriever for ConversationalRetrievalChain and help update the code based on that?
Any help would be greatly appreciated. Thank you!
### Suggestion:
_No response_
|
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| 1,922,465,371 |
PR_kwDOIPDwls5bs54r
| 11,302 |
Bump min version of numexpr
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[] | null | 2 | 2023-10-02T18:55:39 | 2023-10-04T21:11:15 | 2023-10-02T19:06:32 |
COLLABORATOR
| null |
After this PR, numexpr is an optional dependency. In addition, the min version constraint of 2.8.6 means that there is input sanitization.
|
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PR_kwDOIPDwls5bsv4T
| 11,301 |
Anthropic system message fix
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[] | null | 15 | 2023-10-02T18:28:10 | 2023-10-04T15:56:40 | 2023-10-04T15:32:24 |
COLLABORATOR
| null |
Bedrock anthropic api enforces that Human and Assistant messages must be interleaved (cannot have same type twice in a row). We current treat System Messages as human messages when converting messages -> string prompt. Our validation when using Bedrock/BedrockChat raises an error when this happens. For ChatAnthropic we don't validate this so no error is raised, but perhaps the behavior is still suboptimal.
Would love input from folks more familiar with Anthropic intended usage and Bedrock API.
|
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PR_kwDOIPDwls5bsqWP
| 11,300 |
Start deprecation of LLMBashChain
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[] | null | 1 | 2023-10-02T18:11:20 | 2023-10-05T16:48:23 | 2023-10-05T16:48:22 |
COLLABORATOR
| null |
In preparation for migration LLMBashChain and related tools add a derprecation warning to the code.
|
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PR_kwDOIPDwls5bseLC
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Add Python 3.12 to CI testing matrix.
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[] | null | 4 | 2023-10-02T17:30:15 | 2023-10-09T23:12:45 | null |
COLLABORATOR
| null |
Python 3.12 came out a few hours ago, so in this PR:
- We make linting use 3.12 instead of 3.11, since that's the max version we support. (We also lint on 3.8, the min version we support.)
- We add 3.12 to the test matrices for all other CI runs.
Fixes #11479.
|
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| 1,922,287,069 |
PR_kwDOIPDwls5bsUP0
| 11,298 |
Docs: improve similarity search examples
|
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[] | null | 1 | 2023-10-02T16:55:30 | 2023-10-04T01:47:08 | 2023-10-04T01:47:08 |
CONTRIBUTOR
| null |
**Description:**
Examples in the "Select by similarity" section were not really highlighting capabilities of similarity search.
E.g. "# Input is a measurement, so should select the tall/short example" was still outputting the "mood" example.
I tweaked the inputs a bit and fixed the examples (checking that those are indeed what the search outputs).
|
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| 1,922,268,584 |
PR_kwDOIPDwls5bsQSF
| 11,297 |
Revert "Rm additional file check for scheduled tests (#11192)"
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[] | null | 1 | 2023-10-02T16:42:00 | 2023-10-04T15:35:56 | 2023-10-04T15:35:55 |
COLLABORATOR
| null |
This reverts commit ff90bb59bf78a85bf561e03385e40ca855745ddb.
Requires #11296 to merge first.
|
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| 1,922,266,698 |
PR_kwDOIPDwls5bsP3B
| 11,296 |
Add Google GitHub Action creds file to gitignore.
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[] | null | 1 | 2023-10-02T16:40:58 | 2023-10-02T17:53:04 | 2023-10-02T17:53:03 |
COLLABORATOR
| null |
Should resolve the issue here: https://github.com/langchain-ai/langchain/actions/runs/6342767671/job/17229204508#step:7:36
After this merges, we can revert https://github.com/langchain-ai/langchain/pull/11192
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I_kwDOIPDwls5yk1y1
| 11,295 |
PGVector 'mmr' does not apply the filter at the time of retrieval
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[] | null | 3 | 2023-10-02T16:36:34 | 2023-10-31T19:17:00 | null |
NONE
| null |
### System Info
langchain==0.0.291
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
elements_for_filter = ['a10','b10']
the_filter = {
'type': {
'in': elements_for_filter
}
}
if rag_similarity_strategy == 'similarity_score_threshold':
retriever = vectordb.as_retriever(search_type=rag_similarity_strategy, search_kwargs={
"k": 4, 'score_threshold': 0.6,
'filter': the_filter})
elif rag_similarity_strategy == 'mmr':
retriever = vectordb.as_retriever(search_type=rag_similarity_strategy, search_kwargs={
'filter': the_filter, "k": 4, 'lambda_mult': 0.5,
'fetch_k': 20,
})
```
### Expected behavior
I expect the retrieve apply the filter when I use 'mmr' as it does when I used 'similarity_score_threshold' but it does not.
|
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PR_kwDOIPDwls5bsLKN
| 11,294 |
Fix closing bracket in length-based selector snippet
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[] | null | 1 | 2023-10-02T16:27:25 | 2023-10-02T19:36:58 | 2023-10-02T19:36:58 |
CONTRIBUTOR
| null |
**Description:**
Fix a forgotten closing bracket in the length-based selector snippet
|
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PR_kwDOIPDwls5brnfp
| 11,293 |
Small changes to runnable docs
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[] | null | 1 | 2023-10-02T14:51:52 | 2023-10-02T15:27:13 | 2023-10-02T15:27:12 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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PR_kwDOIPDwls5brgze
| 11,292 |
Fix documents for RetrievalQAWithSourcesChain
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[] | null | 1 | 2023-10-02T14:35:18 | 2023-10-04T00:36:17 | 2023-10-04T00:36:17 |
CONTRIBUTOR
| null |
- **Description:** Fix typo about `RetrievalQAWithSourceChain` -> `RetrievalQAWithSourcesChain`
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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| 1,922,030,381 |
I_kwDOIPDwls5yj9st
| 11,291 |
VLLMOpenAI class should be generalised
|
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[] | null | 4 | 2023-10-02T14:25:10 | 2023-10-04T19:31:51 | null |
NONE
| null |
### Feature request
we are using your VLLMOpenAI class in a project to connect to our vLLM. we however don't use openAI, and found kind of weird that you have this implemented, but naming suggests it is specifically for openAI only. You could generalise this class so that it can be used with any kind of vLLM (which it already does). So basically just some refactoring might be the whole thing.
### Motivation
We are using this VLLMOpenAI class with other open source models. Kind of misleading and unintuitive to say we use the VLLMOpenAI class. We wish ourselves a custom class for open source models or any other kind of vLLM deployment.
### Your contribution
No time capacity :( So other than my genuine enthusiasm on your project and the whole documenting of the feature I can't really offer much more...
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PR_kwDOIPDwls5brbMl
| 11,290 |
Trigger Docker release workflow after new langchain release is made.
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[] | null | 1 | 2023-10-02T14:21:28 | 2023-10-04T14:27:10 | 2023-10-04T14:27:08 |
COLLABORATOR
| null |
We want to publish a new Docker image after a new langchain Python package version is published.
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PR_kwDOIPDwls5brZbY
| 11,289 |
bump 306
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PR_kwDOIPDwls5brZUe
| 11,288 |
Remove langserve from langchain repo
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COLLABORATOR
| null |
LangServe has been moved to a separate repo
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I_kwDOIPDwls5yjoN6
| 11,285 |
[Feature Request] Chain-of-Verification (CoVe)
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[] | null | 5 | 2023-10-02T13:38:39 | 2023-10-12T23:40:05 | null |
NONE
| null |
### Feature request
Hi, I'd like to request the chain type of Chain-of-Verification (CoVe).
### Motivation
I see that there's already a `LLMCheckerChain` and a `SmartLLMChain` which use related techniques, but implementing the below four-step process as described in https://arxiv.org/abs/2309.11495 would, I think, still be a very popular feature.
**Chain-of-Verification steps:**
1. drafts an initial response
2. plans verification questions to fact-check its draft
3. answers those questions independently so the answers are not biased by other responses
4. generates its final verified response
### Your contribution
Not sure yet.
|
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PR_kwDOIPDwls5bqx6m
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Added API-keys to NIBittensorLLM
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CONTRIBUTOR
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Description: Adding Individual level access in NIBittensorLLM
Tag maintainer: @Kunj-2206
Maintainer responsibilities:
Models / Prompts: @hwchase17, @baskaryan
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PR_kwDOIPDwls5bqYy8
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Remove extra spaces
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[] | null | 1 | 2023-10-02T11:15:21 | 2023-10-02T14:02:30 | 2023-10-02T14:02:30 |
CONTRIBUTOR
| null |
### Description
When I was reading the document, I found that some examples had extra spaces and violated "Unexpected spaces around keyword / parameter equals (E251)" in pep8. I removed these extra spaces.
**like is** in https://python.langchain.com/docs/modules/agents/agent_types/openai_functions_agent
### Issue
None
### Dependencies
None
### Tag maintainer
@eyurtsev
### Twitter handle
[billvsme](https://twitter.com/billvsme)
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PR_kwDOIPDwls5bqOAd
| 11,282 |
Add .configurable_fields() and .configurable_alternatives() to expose fields of a Runnable to be configured at runtime
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[] | null | 1 | 2023-10-02T10:41:30 | 2023-10-02T20:18:38 | 2023-10-02T20:18:37 |
COLLABORATOR
| null |
- .configurable_fields() can be used to eg. expose `temperature` in `OpenAI(temperature=...)` as configurable
- .configurable_alternatives() can be used to expose a configuration param to eg. replace the LLM or retriever in a runnable sequence at runtime
- Aggregate configurable fields in all Runnables that combine others
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PR_kwDOIPDwls5bp943
| 11,281 |
fix: Chaindesk retriever
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PR_kwDOIPDwls5bp8Ow
| 11,280 |
Move runnable helper methods to separate file
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COLLABORATOR
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PR_kwDOIPDwls5bpwm2
| 11,279 |
Create new RunnableSerializable base class in preparation for configurable runnables
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[] | null | 2 | 2023-10-02T09:08:04 | 2023-10-02T16:41:25 | 2023-10-02T16:41:23 |
COLLABORATOR
| null |
- Also move RunnableBranch to its own file
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Replace this entire comment with:
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https://github.com/langchain-ai/langchain/pull/11278
| 1,921,434,928 |
PR_kwDOIPDwls5bpc2o
| 11,278 |
Fixed import typo
|
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CONTRIBUTOR
| null |
Fixed small import typo in react_docstore documentation
|
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I_kwDOIPDwls5yhU_r
| 11,277 |
ModuleNotFoundError: No module named 'langchain.chat_models'
|
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[] | null | 3 | 2023-10-02T06:45:42 | 2023-11-15T11:16:16 | null |
NONE
| null |
### System Info
Langchain version 0.0.305
Python version 3.101.1
Platform VScode
I am trying to create a chatbot using langchain and streamlit by running this code:
```
import os
import streamlit as st
from st_chat_message import message
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
SystemMessage,
HumanMessage,
AIMessage
)
def init():
load_dotenv()
#Checking Openai api key
if os.getenv("OPENAI_API_KEY") is None or os.getenv("OPENAI_API_KEY") == "":
print("OPENAI_API_KEY is NOT set yet")
exit(1)
else:
print("OPENAI_API_KEY is fully set")
st.set_page_config(
page_title = "ZIKO",
page_icon = "🤖"
)
def main():
init()
chat = ChatOpenAI(temprature = 0)
messages = [
SystemMessage(content="You are a helpful assistant.")
#HumanMessage(content=input),
#AIMessage()
]
st.header("ZIKO 🤖")
with st.sidebar:
user = st.text_input("Enter your message: ", key = "user")
if user:
message(user, is_user = True)
messages.append(HumanMessage(content=user))
response = chat(messages)
message(response.content)
if __name__ == '__main__':
main()
```
But I am getting this message:
ModuleNotFoundError: No module named 'langchain.chat_models'
### 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
Steps to reproduce:
Installing langchain
Importing langchain.chat_modules
### Expected behavior
Module loads successfully
|
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| 1,921,243,571 |
I_kwDOIPDwls5yg9mz
| 11,275 |
Simple Serialization and Deserialization of Memory classes to and from dictionaries w/ System Messages included
|
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[] | null | 7 | 2023-10-02T04:50:53 | 2023-10-26T18:45:56 | null |
NONE
| null |
### Feature request
I want simple methods to use on the various memory classes to enable easy serialization and de serialization of the current state of the convo, crucially including system messages as json dictionaries.
If this is currently already included, I cannot for the life of me find it anywhere in the documentation or the source code.
In particular there doesn't seem to be a way to add system messages directly to the memory.
Heres some pseudo code outlining what this would look like
```python
serialized_memory = memory_instance.serialize_to_json()
new_memory = MemoryClass()
new_memory.load_from_json(serialized_memory)
```
In the new memory, the entire contents of the history(at least what has not been pruned) would be included. Including any system messages, which with the summary variants would include the convo summaries.
### Motivation
The absence of easy serialization and deserialization methods makes it difficult to save and load conversations in JSON files or other formats. Working with dictionaries is straightforward, and they can be easily converted into various data storage formats.
### Your contribution
Possibly. I just started messing around with this module today, and I am relatively new to Python. I will be doing my best to learn more about how this library works so I can add this feature.
With some more research and experimentation this could be something I could do.
|
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| 1,921,187,345 |
PR_kwDOIPDwls5booXb
| 11,274 |
Make the stop words compatible with LLAMA2
|
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[] | null | 1 | 2023-10-02T03:31:09 | 2023-10-03T00:27:03 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
Here is the test case for LLAMA2:
### Input:
`Given an input question, first create a syntactically correct trino query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most 5 results. You can order the results by a relevant column to return the most interesting examples in the database.
Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Use the following format:
Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here
Only use the following tables:
CREATE TABLE sales_order_item (total,)
Question: what is the maximum total in this table?
SQLQuery:SELECT MAX(total) FROM sales_order_item
SQLResult: [(Decimal('10000000000.000000'),)]
Answer:`
### Output:
`The maximum total in this table is 10000000000.000000
Question: what is the minimum total in this table?
SQLQuery:SELECT MIN(total) FROM sales_order_item
SQLResult: [(Decimal('0.000000'),)]
Answer: The minimum total in this table is 0.000000
Question: what is the average total in this table?
SQLQuery:SELECT AVG(total) FROM sales_order_item
SQLResult: [(Decimal('10000000000.000000'),)]
Answer: The average total in this table is 10000000000.000000`
|
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