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https://api.github.com/repos/langchain-ai/langchain/issues/11272
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| 1,921,054,820
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I_kwDOIPDwls5ygPhk
| 11,272
|
Another Languaje
|
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| false
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| 2023-10-02T00:27:49
| 2023-10-02T02:29:48
| null |
NONE
| null |
### Feature request
Is possible utilizate another languaje, like spanish?
### Motivation
Another Languaje
### Your contribution
I don't
|
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PR_kwDOIPDwls5boGS0
| 11,271
|
johnsnowlabs embeddings support
|
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| 2023-10-01T23:19:17
| 2023-10-27T03:22:50
| 2023-10-27T03:22:50
|
CONTRIBUTOR
| null |
- **Description:** Introducing the [JohnSnowLabsEmbeddings](https://www.johnsnowlabs.com/)
- **Dependencies:** johnsnowlabs
- **Tag maintainer:** @C-K-Loan
- **Twitter handle:** https://twitter.com/JohnSnowLabs https://twitter.com/ChristianKasimL
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| 1,920,999,592
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PR_kwDOIPDwls5boA7h
| 11,270
|
Fix line break in docs imports
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| 2023-10-02T19:37:17
| 2023-10-02T19:37:16
|
CONTRIBUTOR
| null |
It is just a straightforward docs fix.
|
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| 1,920,937,186
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PR_kwDOIPDwls5bn0OZ
| 11,269
|
Consolidate run collector. Add link helper
|
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| 2023-10-01T19:43:09
| 2023-10-03T13:21:00
| 2023-10-03T13:20:59
|
COLLABORATOR
| null |
Instead of:
```
client = Client()
with collect_runs() as cb:
chain.invoke()
run = cb.traced_runs[0]
client.get_run_url(run)
```
it's
```
with tracing_v2_enabled() as cb:
chain.invoke()
cb.get_run_url()
```
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I_kwDOIPDwls5yfmeF
| 11,268
|
Chat cache results in KeyError: 'message'
|
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| 2023-10-01T17:48:12
| 2023-11-10T11:17:00
| null |
NONE
| null |
### System Info
I'm trying to use langchain with cache using gptcache, using pydantic models
(prior to this message I'm building prompt of mixed human and system messages)
```
chain = create_structured_output_chain(Job, llm, prompt, verbose=False)
res = chain.run({'job': job})
```
when not using cache everything seems to work but when getting from cache I'm seeing this error:
```
File "/Users/orlevy/Projects/tfg/llm-service/venv/lib/python3.9/site-packages/langchain/schema/output.py", line 62, in set_text
values["text"] = values["message"].content
KeyError: 'message'
```
When I digged a little further it seems like this code, is not generating the same response (we are losing function call response) :
<img width="661" alt="image" src="https://github.com/langchain-ai/langchain/assets/21291210/cfeccb45-4806-4f85-a01c-67b42a882c8d">
modified code a bit(not sure if this is actually a good idea) but this one seems to work for chat:
```
res = get(prompt, cache_obj=_gptcache)
if res:
return [
ChatGeneration(
message=AIMessage(content='',
additional_kwargs=generation_dict['message']['additional_kwargs']
),
generation_info=dict(finish_reason=generation_dict.get("finish_reason")),
) for generation_dict in json.loads(res)
]
```
would really appreciate
your help with this
### 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
1. use chat with chain messages
2. use cachegpt (used postgres)
3. make one call
4. cache should be populated
5. from that point on when using cache we see error
### Expected behavior
I should be able to use cache
|
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PR_kwDOIPDwls5bnWrZ
| 11,263
|
feat(graph_sparql_qa): return formatted SPARQL query on demand
|
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| 2023-10-01T14:22:13
| 2023-10-01T14:35:35
| null |
NONE
| null |
- **Description:** Added the `return_sparql_query` feature to the `GraphSparqlQAChain` class, allowing users to get the formatted SPARQL query along with the chain's result.
- **Issue:** NA
- **Dependencies:** None
Note: I've ensured that the PR passes linting and testing by running make format, make lint, and make test locally.
I have added a test for the integration (which relies on network access) and I have added an example to the notebook showing its use.
|
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"url": "https://api.github.com/repos/langchain-ai/langchain/pulls/11263",
"html_url": "https://github.com/langchain-ai/langchain/pull/11263",
"diff_url": "https://github.com/langchain-ai/langchain/pull/11263.diff",
"patch_url": "https://github.com/langchain-ai/langchain/pull/11263.patch",
"merged_at": null
}
|
https://api.github.com/repos/langchain-ai/langchain/issues/11262
|
https://api.github.com/repos/langchain-ai/langchain
|
https://api.github.com/repos/langchain-ai/langchain/issues/11262/labels{/name}
|
https://api.github.com/repos/langchain-ai/langchain/issues/11262/comments
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https://api.github.com/repos/langchain-ai/langchain/issues/11262/events
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https://github.com/langchain-ai/langchain/issues/11262
| 1,920,763,561
|
I_kwDOIPDwls5yfIap
| 11,262
|
SystemMessage not updating system prompt
|
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"name": "area: agent",
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"default": false,
"description": "Related to agents module"
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open
| false
| null |
[] | null | 1
| 2023-10-01T13:18:15
| 2023-10-01T13:25:34
| null |
NONE
| null |
I'm using create_conversational_retrieval_agent to create an agent and also passing in a system prompt. However , the prompt is passed in as user message, is this expected behavior?
```
conv_agent_executor = create_conversational_retrieval_agent(
llm, tools, verbose=False,
system_message=SystemMessage(
content="Your name is Votum, an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a factually correct and concise manner unless asked for a detailed explanation. Feel free to use any tools available to look up relevant information, only if necessary")
)
```
Server Logs:
```
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Your are an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a factually correct and concise manner unless asked for a detailed explanation. Feel free to use any tools available to look up relevant information, only if necessary
Answer the following questions as best you can. You have access to the following APIs:
search: Call this tool to interact with the search API. What is the search API useful for? search(query: str) -> str - Useful for retrieving documents related to Indian law. Parameters: {"title": "SearchInput", "type": "object", "properties": {"query": {"title": "Query", "description": "should be a search query in string format", "type": "string"}}, "required": ["query"]}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [search]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can be repeated zero or more times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin!
```
### Suggestion:
_No response_
|
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| 1,920,758,064
|
I_kwDOIPDwls5yfHEw
| 11,261
|
SmartLLMChain errors with Bedrock Anthropic models and n_ideas>1
|
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"name": "area: models",
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open
| false
| null |
[] | null | 7
| 2023-10-01T13:06:07
| 2024-01-09T21:50:51
| null |
NONE
| null |
### System Info
langchain version v0.0.305
Python 3.11
### Who can help?
@hwchase17 @agola11
### Information
- [ ] 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
```python
claudev2 = BedrockChat(
client=bedrock_inference,
model_id="anthropic.claude-v2",
)
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain_experimental.smart_llm import SmartLLMChain
prompt = PromptTemplate.from_template(hard_question)
chain = SmartLLMChain(
llm=claudev2,
prompt=prompt,
n_ideas=2,
)
response = chain.run({} )
print(response)
```
Results in error:
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb) Cell 10 line 1
[5](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=4) prompt = PromptTemplate.from_template(hard_question)
[7](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=6) chain = SmartLLMChain(
[8](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=7) ideation_llm=claudev2,
[9](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=8) critique_llm=claudeinstant,
(...)
[15](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=14) #verbose=True,
[16](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=15) )
---> [18](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=17) response = chain.run({} )
[20](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=19) print(response)
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chains/base.py:507](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chains/base.py:507), in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
505 if len(args) != 1:
506 raise ValueError("`run` supports only one positional argument.")
--> 507 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
508 _output_key
509 ]
511 if kwargs and not args:
512 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
513 _output_key
514 ]
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chains/base.py:312](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chains/base.py:312), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
310 except BaseException as e:
311 run_manager.on_chain_error(e)
--> 312 raise e
313 run_manager.on_chain_end(outputs)
314 final_outputs: Dict[str, Any] = self.prep_outputs(
315 inputs, outputs, return_only_outputs
316 )
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chains/base.py:306](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chains/base.py:306), in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
299 run_manager = callback_manager.on_chain_start(
300 dumpd(self),
301 inputs,
302 name=run_name,
303 )
304 try:
305 outputs = (
--> 306 self._call(inputs, run_manager=run_manager)
307 if new_arg_supported
308 else self._call(inputs)
309 )
310 except BaseException as e:
311 run_manager.on_chain_error(e)
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain_experimental/smart_llm/base.py:166](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain_experimental/smart_llm/base.py:166), in SmartLLMChain._call(self, input_list, run_manager)
164 ideas = self._ideate(stop, run_manager)
165 self.history.ideas = ideas
--> 166 critique = self._critique(stop, run_manager)
167 self.history.critique = critique
168 resolution = self._resolve(stop, run_manager)
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain_experimental/smart_llm/base.py:293](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain_experimental/smart_llm/base.py:293), in SmartLLMChain._critique(self, stop, run_manager)
290 callbacks = run_manager.handlers if run_manager else None
291 if llm:
292 critique = self._get_text_from_llm_result(
--> 293 llm.generate_prompt([prompt], stop, callbacks), step="critique"
294 )
295 _colored_text = get_colored_text(critique, "yellow")
296 _text = "Critique:\n" + _colored_text
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:475](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:475), in BaseChatModel.generate_prompt(self, prompts, stop, callbacks, **kwargs)
467 def generate_prompt(
468 self,
469 prompts: List[PromptValue],
(...)
472 **kwargs: Any,
473 ) -> LLMResult:
474 prompt_messages = [p.to_messages() for p in prompts]
--> 475 return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:365](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:365), in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
363 if run_managers:
364 run_managers[i].on_llm_error(e)
--> 365 raise e
366 flattened_outputs = [
367 LLMResult(generations=[res.generations], llm_output=res.llm_output)
368 for res in results
369 ]
370 llm_output = self._combine_llm_outputs([res.llm_output for res in results])
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:355](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:355), in BaseChatModel.generate(self, messages, stop, callbacks, tags, metadata, run_name, **kwargs)
352 for i, m in enumerate(messages):
353 try:
354 results.append(
--> 355 self._generate_with_cache(
356 m,
357 stop=stop,
358 run_manager=run_managers[i] if run_managers else None,
359 **kwargs,
360 )
361 )
362 except BaseException as e:
363 if run_managers:
File [~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:507](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/~/mambaforge/envs/bedrock/lib/python3.11/site-packages/langchain/chat_models/base.py:507), in BaseChatModel._generate_with_cache(self, messages, stop, run_manager, **kwargs)
503 raise ValueError(
504 "Asked to cache, but no cache found at `langchain.cache`."
505 )
506 if new_arg_supported:
--> 507 return self._generate(
508 messages, stop=stop, run_manager=run_manager, **kwargs
509 )
510 else:
511 return self._generate(messages, stop=stop, **kwargs)
[/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb) Cell 10 line 3
[366](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=365) if stop:
[367](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=366) params["stop_sequences"] = stop
--> [369](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=368) completion = self._prepare_input_and_invoke(
[370](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=369) prompt=prompt, stop=stop, run_manager=run_manager, **params
[371](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=370) )
[373](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=372) message = AIMessage(content=completion)
[374](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=373) return ChatResult(generations=[ChatGeneration(message=message)])
[/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb) Cell 10 line 2
[223](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=222) provider = self._get_provider()
[224](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=223) params = {**_model_kwargs, **kwargs}
--> [225](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=224) input_body = LLMInputOutputAdapterv2.prepare_input(provider, prompt, params)
[226](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=225) body = json.dumps(input_body)
[227](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=226) accept = "application/json"
[/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb) Cell 10 line 7
[73](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=72) input_body = {**model_kwargs}
[74](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=73) if provider == "anthropic":
---> [75](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=74) input_body["prompt"] = _human_assistant_format_v2(prompt)
[76](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=75) elif provider == "ai21":
[77](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=76) input_body["prompt"] = prompt
[/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb](https://file+.vscode-resource.vscode-cdn.net/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb) Cell 10 line 4
[47](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=46) count += 1
[48](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=47) else:
---> [49](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=48) raise ValueError(ALTERNATION_ERROR)
[51](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=50) if count % 2 == 1: # Only saw Human, no Assistant
[52](vscode-notebook-cell:/Users/austinmw/Desktop/bedrocktesting/bedrock_test.ipynb#X14sZmlsZQ%3D%3D?line=51) input_text = input_text + ASSISTANT_PROMPT # SILENT CORRECTION
ValueError: Error: Prompt must alternate between '
Human:' and '
Assistant:'.
```
### Expected behavior
No error
|
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I_kwDOIPDwls5yfECm
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Sub Question Query Engine
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[] | null | 3
| 2023-10-01T12:37:44
| 2023-12-07T05:49:37
| null |
NONE
| null |
### Feature request
Hi, does langchain have a feature similar to LlamaIndex's [Sub Question Query Engine](https://gpt-index.readthedocs.io/en/latest/examples/query_engine/sub_question_query_engine.html)? If not, I'd like to submit a feature request!
### Motivation
This capability is very useful!
### Your contribution
Not sure.
|
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| 1,920,745,420
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PR_kwDOIPDwls5bnM8Y
| 11,259
|
rename repo namespace to langchain-ai
|
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| 2023-10-01T12:37:15
| 2023-10-01T19:31:06
| 2023-10-01T19:30:58
|
CONTRIBUTOR
| null |
### Description
renamed several repository links from `hwchase17` to `langchain-ai`.
### Why
I discovered that the README file in the devcontainer contains an old repository name, so I took the opportunity to rename the old repository name in all files within the repository, excluding those that do not require changes.
### Dependencies
none
### Tag maintainer
@baskaryan
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
|
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I_kwDOIPDwls5ye2En
| 11,258
|
ChatOpenAI: Exception InvalidRequestError changes bound "self"
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[] | null | 2
| 2023-10-01T10:23:41
| 2023-10-01T11:52:12
| 2023-10-01T10:52:33
|
NONE
| null |
### System Info
|Name|Detail|
|--------|--------|
| Python | `3.10.13` |
| LangChain | `0.0.301` |
| System | MacOS `12.2.1` |
### Bug Detail
Playing around with a recursive function to decrement value `k` and ran into an issue whereby `self` is overwritten and bound to instance of `openai.error.InvalidRequestError` when the exception is caught. Note in the code below, the `AttributeError` exception is thrown when `self.save()` is executed upon a successful `run()` or `try` where `openai.error.InvalidRequestError` is not thrown.
### 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
**The code below is a gross oversimplification, but it illustrates how I found this bug:**
```python
class SomeModel(models.Model):
query = models.CharField(max_length=255)
response = models.TextField()
def save(self, *args, **kwargs):
# Unmodified Django save method
return super().save(*args, **kwargs)
def run(self, kb, k=5):
try:
llm = ChatOpenAI()
chain = load_qa_chain(llm=llm, chain_type='stuff')
self.response = chain.run(input_documents=[], human_input=self.query)
self.save() # AttributeError: 'InvalidRequestError' object has no attribute 'save'
except (openai.error.InvalidRequestError,) as e:
if k < 2:
raise e
new_k_val = k - 1
print('K value too high. Trying with %s' % new_k_val)
return self.run(kb, k=new_k_val)
```
### Expected behavior
Where exception happens, `self` should not be bound to another instance/instance of `openai.error.InvalidRequestError`
|
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PR_kwDOIPDwls5bm7h5
| 11,257
|
[fix] create redis vector index before adding docs, add prefix to doc…
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[] | null | 7
| 2023-10-01T09:19:07
| 2023-10-24T17:51:26
| 2023-10-24T17:51:25
|
CONTRIBUTOR
| null |
Fix Description:
For Redis Vector integration in add_texts method, there were two issues that lead to this bug.
1. Vector index is not being created leading to no such_index error
2. `doc:index` prefix was also missing for Redis Keys.
resolves #11197
Maintainer: @baskaryan
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| 1,920,632,960
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PR_kwDOIPDwls5bm1wb
| 11,256
|
Fix typo in docstring
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[] | null | 1
| 2023-10-01T08:11:13
| 2023-10-01T19:55:12
| 2023-10-01T19:55:12
|
CONTRIBUTOR
| null |
Description : Remove meaningless 's' in docstring
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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| 1,920,545,447
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PR_kwDOIPDwls5bmjpE
| 11,255
|
use jq schema for content_key in json_loader
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| 2023-10-01T04:12:17
| 2023-10-05T15:11:06
| null |
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
- Twitter handle: we announce bigger features on Twitter. If your PR gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
### Description
Changed the value specified for `content_key` in JSONLoader from a single key to a value based on jq schema.
Sorry, I created the [same Pull Request](https://github.com/langchain-ai/langchain/pull/8075) in the past, but some time has elapsed and it was closed, so I am recreating it.
### Why
For json data like the following, specify `.data[].attributes.message` for page_content and `.data[].attributes.id` or `.data[].attributes.attributes. tags`, etc., the `content_key` must also parse the json structure.
<details>
<summary>sample json data</summary>
```json
{
"data": [
{
"attributes": {
"attributes": {
"dd": {
"service": "worker",
"env": "production",
"version": "6b2c46a5883a9097aa2cb09907786b5c06ca3bd0"
},
"source": "stderr",
"service": "worker",
"name": "app.services.predict_services",
"levelname": "ERROR",
"container_id": "f9f880b243cc41f1a7d9bae5bf922d60-1262558729",
"timestamp": 1686679407827.0
},
"message": "error while processing user #19084: 'numpy.dtype[bool_]' object is not callable",
"service": "worker",
"status": "error",
"tags": [
"datadog.submission_auth:private_api_key",
"env:production"
],
"timestamp": "2023-06-14T03:03:27.827000+09:00"
},
"id": "AgAAAYi17UzTUeIczgAAAAAAAAAYAAAAAEFZaTE3VThXQUFEOF9sS2J4Z3psRmdBRAAAACQAAAAAMDE4OGI2MzYtMmZlNC00ZDEwLThjZDMtMzhkZTI0NmUyNWMz",
"type": "log"
},
{
"attributes": {
"attributes": {
"dd": {
"service": "worker",
"env": "production",
"version": "6b2c46a5883a9097aa2cb09907786b5c06ca3bd0"
},
"process": 42.0,
"messages": "error while processing user #19084: 'numpy.dtype[bool_]' object is not callable",
"levelname": "ERROR",
"container_id": "f9f880b243cc41f1a7d9bae5bf922d60-1262558729",
"timestamp": 1686679407831.0
},
"message": "{\"messages\": \"error while processing user #19084: 'numpy.dtype[bool_]' object is not callable\"}",
"service": "worker",
"status": "error",
"tags": [
"datadog.submission_auth:private_api_key",
"env:production"
],
"timestamp": "2023-06-14T03:03:27.831000+09:00"
},
"id": "AgAAAYi17UzXUeIczwAAAAAAAAAYAAAAAEFZaTE3VThXQUFEOF9sS2J4Z3psRmdBRQAAACQAAAAAMDE4OGI2MzYtMmZlNC00ZDEwLThjZDMtMzhkZTI0NmUyNWMz",
"type": "log"
}
],
"meta": {
"elapsed": 26,
"request_id": "pddv1ChY0SmttaDA0a1REeXZRM01yNkFwYnd3Ii0KHWIANr8mghGpsMIX2cOarI6t4WyTVObXx3wrAuudEgzSbtmduLtPxFVkSo0",
"status": "done"
}
}
```
</details>
<details>
<summary>sample code</summary>
```python
def metadata_func(record: dict, metadata: dict) -> dict:
print(record)
metadata["id"] = record.get("id")
metadata["tags"] = record["attributes"].get("tags")
return metadata
sample_file = "sample.json"
loader = JSONLoader(
file_path=sample_file,
jq_schema=".data[]",
content_key=".attributes.message",
metadata_func=metadata_func
)
```
</details>
### Dependencies
none
### Tag maintainer
@rlancemartin, @eyurtsev
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
|
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PR_kwDOIPDwls5bmWcV
| 11,254
|
Fix json loader
|
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[] | null | 2
| 2023-10-01T01:34:09
| 2023-10-01T01:49:22
| 2023-10-01T01:39:07
|
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer (see below),
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Please make sure you're PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
If you're adding a new integration, please include:
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If no one reviews your PR within a few days, feel free to @-mention the same people again.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
### Description
Changed the value specified for `content_key` in JSONLoader from a single key to a value based on jq schema.
Sorry, I created the [same Pull Request](https://github.com/langchain-ai/langchain/pull/8075) in the past, but some time has elapsed and it was closed, so I am recreating it.
### Why
For json data like the following, specify `.data[].attributes.message` for page_content and `.data[].attributes.id` or `.data[].attributes.attributes. tags`, etc., the `content_key` must also parse the json structure.
<details>
<summary>sample json data</summary>
```json
{
"data": [
{
"attributes": {
"attributes": {
"dd": {
"service": "worker",
"env": "production",
"version": "6b2c46a5883a9097aa2cb09907786b5c06ca3bd0"
},
"source": "stderr",
"service": "worker",
"name": "app.services.predict_services",
"levelname": "ERROR",
"container_id": "f9f880b243cc41f1a7d9bae5bf922d60-1262558729",
"timestamp": 1686679407827.0
},
"message": "error while processing user #19084: 'numpy.dtype[bool_]' object is not callable",
"service": "worker",
"status": "error",
"tags": [
"datadog.submission_auth:private_api_key",
"env:production"
],
"timestamp": "2023-06-14T03:03:27.827000+09:00"
},
"id": "AgAAAYi17UzTUeIczgAAAAAAAAAYAAAAAEFZaTE3VThXQUFEOF9sS2J4Z3psRmdBRAAAACQAAAAAMDE4OGI2MzYtMmZlNC00ZDEwLThjZDMtMzhkZTI0NmUyNWMz",
"type": "log"
},
{
"attributes": {
"attributes": {
"dd": {
"service": "worker",
"env": "production",
"version": "6b2c46a5883a9097aa2cb09907786b5c06ca3bd0"
},
"process": 42.0,
"messages": "error while processing user #19084: 'numpy.dtype[bool_]' object is not callable",
"levelname": "ERROR",
"container_id": "f9f880b243cc41f1a7d9bae5bf922d60-1262558729",
"timestamp": 1686679407831.0
},
"message": "{\"messages\": \"error while processing user #19084: 'numpy.dtype[bool_]' object is not callable\"}",
"service": "worker",
"status": "error",
"tags": [
"datadog.submission_auth:private_api_key",
"env:production"
],
"timestamp": "2023-06-14T03:03:27.831000+09:00"
},
"id": "AgAAAYi17UzXUeIczwAAAAAAAAAYAAAAAEFZaTE3VThXQUFEOF9sS2J4Z3psRmdBRQAAACQAAAAAMDE4OGI2MzYtMmZlNC00ZDEwLThjZDMtMzhkZTI0NmUyNWMz",
"type": "log"
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"status": "done"
}
}
```
</details>
<details>
<summary>sample code</summary>
```python
def metadata_func(record: dict, metadata: dict) -> dict:
print(record)
metadata["id"] = record.get("id")
metadata["tags"] = record["attributes"].get("tags")
return metadata
sample_file = "sample.json"
loader = JSONLoader(
file_path=sample_file,
jq_schema='.data[].attributes.message',
content_key="data",
metadata_func=metadata_func
)
```
</details>
### Dependencies
none
### Tag maintainer
@rlancemartin, @eyurtsev
### Twitter handle
[kzk_maeda](https://twitter.com/kzk_maeda)
|
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| 1,920,348,709
|
I_kwDOIPDwls5ydjIl
| 11,252
|
Implement a custom LangChain class wrapper (LLM model/Agent) for StarCoder model
|
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[] | null | 11
| 2023-09-30T17:57:16
| 2023-11-29T05:38:36
| null |
NONE
| null |
### Feature request
Is it possible to integrate any language model (LM) trained on source code or opensource codebases on various programming languages e.g. :
- [StarCoder](https://github.com/bigcode-project/starcoder)
- [replit/replit-code-v1-3b](https://huggingface.co/replit/replit-code-v1-3b)
- [Bloom](https://bigscience.huggingface.co/blog/bloom)
- etc...
as an [LLM Model](https://python.langchain.com/en/latest/modules/models.html) or an [Agent](https://python.langchain.com/en/latest/modules/agents.html) with [LangChain](https://github.com/hwchase17/langchain), and [chain](https://python.langchain.com/en/latest/modules/chains.html) it in a complex usecase?
It would be really cool if, during a general query, the LangChain system has the capability to understand code context (possibly when chained to a vector DB or any data source that contains code or a full code repo), _kind of like chatting with your codebase_!
Any help / hints on the same would be appreciated!
### Motivation
Initially inspired by this [Issue #173](https://github.com/nomic-ai/gpt4all/issues/173)
Raised a similar Issue long back with StarCoder [Issue #53](https://github.com/bigcode-project/starcoder/issues/53)
### Your contribution
n/a
|
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PR_kwDOIPDwls5blzrN
| 11,251
|
Clarifai integration doc improvements
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| 2023-10-04T06:05:03
| 2023-10-04T01:47:57
|
CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** Doc corrections and resolve notebook rendering issue on GH
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan
- **Twitter handle:** `@isaacchung1217`
|
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| 1,920,300,925
|
I_kwDOIPDwls5ydXd9
| 11,250
|
Error importing DynamoDBChatMessageHistory
|
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[] | null | 2
| 2023-09-30T15:34:02
| 2023-12-30T16:01:25
| null |
NONE
| null |
### System Info
I am following this example: https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history
When attempting to import DynamoDBChatMessageHistory I am receiving an error
`from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory`
`---------------------------------------------------------------------------
PydanticUserError Traceback (most recent call last)
/var/folders/jq/w80pdyz11zbbhbvz7p8795gh0000gn/T/ipykernel_9395/3590968237.py in <module>
----> 1 from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory
2
3 # history = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="0")
4
5 # history.add_user_message("hi!")
/usr/local/lib/python3.9/site-packages/langchain/__init__.py in <module>
4 from typing import Optional
5
----> 6 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
7 from langchain.cache import BaseCache
8 from langchain.chains import (
/usr/local/lib/python3.9/site-packages/langchain/agents/__init__.py in <module>
1 """Interface for agents."""
----> 2 from langchain.agents.agent import (
3 Agent,
4 AgentExecutor,
5 AgentOutputParser,
/usr/local/lib/python3.9/site-packages/langchain/agents/agent.py in <module>
13 from pydantic import BaseModel, root_validator
14
---> 15 from langchain.agents.tools import InvalidTool
16 from langchain.base_language import BaseLanguageModel
17 from langchain.callbacks.base import BaseCallbackManager
/usr/local/lib/python3.9/site-packages/langchain/agents/tools.py in <module>
2 from typing import Optional
3
----> 4 from langchain.callbacks.manager import (
5 AsyncCallbackManagerForToolRun,
6 CallbackManagerForToolRun,
/usr/local/lib/python3.9/site-packages/langchain/callbacks/__init__.py in <module>
1 """Callback handlers that allow listening to events in LangChain."""
2
----> 3 from langchain.callbacks.aim_callback import AimCallbackHandler
4 from langchain.callbacks.clearml_callback import ClearMLCallbackHandler
5 from langchain.callbacks.comet_ml_callback import CometCallbackHandler
/usr/local/lib/python3.9/site-packages/langchain/callbacks/aim_callback.py in <module>
2 from typing import Any, Dict, List, Optional, Union
3
----> 4 from langchain.callbacks.base import BaseCallbackHandler
5 from langchain.schema import AgentAction, AgentFinish, LLMResult
6
/usr/local/lib/python3.9/site-packages/langchain/callbacks/base.py in <module>
5 from uuid import UUID
6
----> 7 from langchain.schema import (
8 AgentAction,
9 AgentFinish,
/usr/local/lib/python3.9/site-packages/langchain/schema.py in <module>
145
146
--> 147 class ChatGeneration(Generation):
148 """Output of a single generation."""
149
/usr/local/lib/python3.9/site-packages/langchain/schema.py in ChatGeneration()
152
153 @root_validator
--> 154 def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:
155 values["text"] = values["message"].content
156 return values
/usr/local/lib/python3.9/site-packages/pydantic/deprecated/class_validators.py in root_validator(pre, skip_on_failure, allow_reuse, *__args)
220 if __args:
221 # Ensure a nice error is raised if someone attempts to use the bare decorator
--> 222 return root_validator()(*__args) # type: ignore
223
224 if allow_reuse is True: # pragma: no cover
/usr/local/lib/python3.9/site-packages/pydantic/deprecated/class_validators.py in root_validator(pre, skip_on_failure, allow_reuse, *__args)
226 mode: Literal['before', 'after'] = 'before' if pre is True else 'after'
227 if pre is False and skip_on_failure is not True:
--> 228 raise PydanticUserError(
229 'If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`.'
230 ' Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.',
PydanticUserError: If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`. Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.
For further information visit https://errors.pydantic.dev/2.1.1/u/root-validator-pre-skip`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Attempt to import DynamoDBChatMessageHistory
from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory
### Expected behavior
no errors, successful import
|
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| 1,920,235,293
|
I_kwDOIPDwls5ydHcd
| 11,249
|
MathpixPDFLoader never finishes
|
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open
| false
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[] | null | 6
| 2023-09-30T12:12:22
| 2023-11-28T05:27:02
| null |
NONE
| null |
### System Info
langchain==0.0.305
MacOS M1 Silicon
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have a basic methon for loading the pdf via mathpix
```
def load_pdf(path):
loader = MathpixPDFLoader(path)
pages = loader.load()
return pages;
pages = load_pdf("example_data/DOF-230519AFYXBECVECGY-0028747724.pdf")
```
but if I run this, I gen only this:
Status: None, waiting for processing to complete
Status: None, waiting for processing to complete
Status: None, waiting for processing to complete
Status: None, waiting for processing to complete
Status: None, waiting for processing to complete
Status: None, waiting for processing to complete
Status: None, waiting for processing to complete
...
in backend of mathpix I see the document aldready parsed.
Any idea on this?
### Expected behavior
Expect the document to return.
|
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| 1,920,181,711
|
PR_kwDOIPDwls5blbDp
| 11,248
|
Add comment explaining backend usefulness in gpt4all
|
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[] | null | 1
| 2023-09-30T08:58:27
| 2023-10-01T17:20:25
| null |
NONE
| null |
- **Description:** Adding a comment explaining backend optional attribute usefulness in gpt4all LLM
- **Issue:** it is not clear for the code reader what backend attribute is useful for,
- **Dependencies:** None,
- **Tag maintainer:** *
- **Twitter handle:** @meherhendi
|
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| 1,920,167,736
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PR_kwDOIPDwls5blYVA
| 11,246
|
Doc: fix doc to explain the difference between `OpenAIMultiFunctionsAgent` and `OpenAIFunctionsAgent`
|
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[] | null | 3
| 2023-09-30T08:08:12
| 2023-11-03T01:46:25
| null |
CONTRIBUTOR
| null |
- **Description:** This update adds an explanation detailing the differences between OpenAIMultiFunctionsAgent and OpenAIFunctionsAgent to both the docstring and the documentation of OpenAIMultiFunctionsAgent.
- **Issue:** the issue #8934
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
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PR_kwDOIPDwls5blS-x
| 11,245
|
minor fix: remove redundant code from OpenAIFunctionsAgent
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[] | null | 1
| 2023-09-30T06:23:14
| 2023-10-01T17:22:15
| 2023-10-01T17:22:15
|
CONTRIBUTOR
| null |
Replace this entire comment with:
- **Description:** A very minor fix. Just changed `list([t.name for t in self.tools])` to `[t.name for t in self.tools]` because the `list()` is redundant.
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
|
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| 1,920,074,483
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I_kwDOIPDwls5ycgLz
| 11,244
|
Hierarchical Planner - Observation: requests_put is not a valid tool, try one of [requests_get, requests_post].
|
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open
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[] | null | 5
| 2023-09-30T02:45:21
| 2023-12-30T16:01:56
| null |
NONE
| null |
### System Info
version 0.300
<img width="1397" alt="Screenshot 2023-09-30 at 5 36 41 AM" src="https://github.com/langchain-ai/langchain/assets/17947802/124b1f2a-db02-497f-9cd0-dbad1033b6c8">
\\nObservation: requests_put is not a valid tool, try one of [requests_get, requests_post].\\nThought:I need to use requests_post to update the pet\'s ship date.\\n\\nAction:
This keep happening time and time again!
### Who can help?
hierarchical planner, requests agent
https://github.com/openchatai/OpenCopilot/blob/ecd2f51bfc1f989133a44c4cff15d858b0d2fd30/llm-server/api_caller/planner.py#L319
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
--
### Expected behavior
The agent should be able to use the `put` tool when it needs to use it
|
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| 1,919,979,559
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I_kwDOIPDwls5ycJAn
| 11,243
|
Limit Data and Enforcing Security for SQLDatabase & Agents
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[] | null | 17
| 2023-09-29T22:31:23
| 2023-12-18T11:05:25
| null |
NONE
| null |
### Feature request
We all want to have our clients to interact with the database using LLM, langchain with create_sql_agent, are great, the only problem I see, there is no way to limit user access to the database and fence him to his data.
For example we have table called AI_Product with many columns and one of them is storeid, for normal user, he should have limited access to this table, I also don't trust the AI to limit that, there should be a way to programmatically do that.
So let's look at AI_Product
```
id, name, price, store_id
1, water, 2, 10001
2, pepsi, 4, 10001
3, cokezero , 5, 10002
```
For the above table, the user only have rights to access storeid= 10001, for security and true data we need a way to limit what this user can see.
One solution could be is to allow include_tables to have sql
`include_tables = ['AI_Product']
`
to
`include_tables = ['(select * from AI_Product where store_id = '10001') AI_Product']
`
The other one, is allow the SQL query to come back before execution and allow me to modify it
So if I asked , "How many products do we have?"
```
agent: sql:"select count(*) from AI_Product"
Then we can modify the sql to
protected_sql = sql.replace("AI_Product", "(select * from AI_Product where store_id = '10001') AI_Product")
#sql = "select count(*) from (select * from AI_Product where store_id = '10001') AI_Product"
agent.run (protected_sql)
```
### Motivation
Find a way to protect the data while allow end-user unlimited access to it.
### Your contribution
I'm not familiar with the internal code, but I can help if gudied
|
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| 1,919,948,621
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I_kwDOIPDwls5ycBdN
| 11,242
|
Issue: < Urgent Help needed related to VectorstoreIndexCreator >
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[] | null | 2
| 2023-09-29T21:53:43
| 2023-10-31T07:58:55
| 2023-10-31T07:58:55
|
NONE
| null |
### Issue you'd like to raise.
while RAG we create a db and then we ask a query to the db in this way,
`index = VectorstoreIndexCreator(
vectorstore_cls=DocArrayInMemorySearch
).from_loaders([loader])`
`index.query(llm=chat, question=query)`
we get some response.
Now lets say wanted to create dataset with the generations ive got from gpt to fintune llama2 llm.
the main problem comes here, now how to know what is the reference we got from the index search as it is totaly abstracted!!
i need the exact querry that gone into llm(gpt) in this process.
Or else can i have any other meathod that solves my problem(tried similarity search and fed reference+query manualy the quality of the output droped so i need index search it self.
### Suggestion:
_No response_
|
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| 1,919,940,380
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PR_kwDOIPDwls5bkp35
| 11,241
|
Update quickstart.mdx to add backtick after `ChatMessages`
|
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[] | null | 1
| 2023-09-29T21:42:08
| 2023-10-02T14:02:04
| 2023-10-02T14:02:03
|
CONTRIBUTOR
| null |
While going through the documentation I found this small issue and wanted to contribute!
<!-- Thank you for contributing to LangChain! -->
|
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PR_kwDOIPDwls5bkpj_
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|
improved rdf_graph schema generation
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[] | null | 3
| 2023-09-29T21:40:09
| 2023-11-16T00:35:24
| null |
NONE
| null |
Description:
The RdfGraph loader provides RDF schema extraction to pass to the GraphSparqlQAChain for Q&A. The RdfGraph also allows to provide short comments to each entity in the schema; I found this very useful to pass context or even small examples on how to use a particular class/attribute to the LLM that enhanced query generation in the GraphSparqlQAChain.
In the current version however, adding comments leads to the generation of several artifacts that make the schema too long to fit the context window of the LLM. For example, adding few comments to a medium size graph led to a schema with 40k tokens from the original 200. This was noted in issue https://github.com/langchain-ai/langchain/issues/8907; although here it refers to OWL format, the problem persists in RDF.
In this PR I am resolving the above issue by adding a filter to the original SPARQL query. Referring to the example above, the schema + comments now takes only 280 tokens.
Another addition is the option to add a minimal list of prefixes to the schema. This is useful when the LLM is not able to infer the correct prefix from the URI or this is completely missing from the schema as I found in few use cases.
I am planning to discuss the use of comments/prefix as an addition to the current documentation in a follow up PR.
Issue: https://github.com/langchain-ai/langchain/issues/8907,
Dependencies: N/A,
Tag maintainer: @jexp ,
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PR_kwDOIPDwls5bkfsZ
| 11,239
|
feat(embeddings): display token usage and cost for OpenAIEmbeddings
|
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| 2023-11-01T14:23:12
| null |
CONTRIBUTOR
| null |
This PR aims at (optionally) displaying the token usage and its cost for `OpenAIEmbeddings`, addressing #11093.
Since I don't see the need to support callbacks for embeddings [^1], I only added the logic to log the number of total tokens (which corresponds to the prompt tokens) and its cost as well as a flag to enable It.
[^1]: It's easy to export or store the embeddings in langchain and I don't think any use case would benefit from "postprocessing the embeddings".
<!-- 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,
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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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PR_kwDOIPDwls5bkeYT
| 11,237
|
make llm imports optional
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COLLABORATOR
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PR_kwDOIPDwls5bkW1g
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Update DeepSparse LLM
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| 2023-09-29T20:13:44
| 2023-09-29T20:55:20
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CONTRIBUTOR
| null |
**Description:** Adds streaming and many more sampling parameters to the DeepSparse interface
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PR_kwDOIPDwls5bkR9F
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Ignore aadd
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| 2023-09-29T19:57:10
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COLLABORATOR
| null |
Appears as an issue from runnables utils
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PR_kwDOIPDwls5bkRES
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Bump deps in langserve
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| 2023-09-29T19:54:06
| 2023-09-29T20:19:39
| 2023-09-29T20:19:38
|
COLLABORATOR
| null |
Bump deps in langserve lockfile
|
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PR_kwDOIPDwls5bkPK0
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fix code injection vuln
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| 2023-09-29T19:45:26
| 2023-09-29T20:16:00
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|
CONTRIBUTOR
| null |
- **Description:** Fix a code injection vuln by adding one more keyword into the filtering list
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:**
- **Twitter handle:**
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PR_kwDOIPDwls5bjkE_
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Add type to message chunks
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COLLABORATOR
| null |
This PR adds types to message chunks to distinguish them from non chunked
message variants
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PR_kwDOIPDwls5bjhVL
| 11,231
|
Make tests stricter, remove old code, fix up pydantic import when using v2
|
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| 2023-09-29T16:45:43
| 2023-09-29T16:47:03
| 2023-09-29T16:47:02
|
COLLABORATOR
| null |
Minor fixes
|
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PR_kwDOIPDwls5bjeO1
| 11,230
|
Feat bedrock cohere support
|
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| 2023-09-29T16:33:33
| 2023-10-04T15:12:20
| 2023-10-04T15:12:20
|
CONTRIBUTOR
| null |
**Description:**
Added support for Cohere command model via Bedrock.
With this change it is now possible to use the `cohere.command-text-v14` model via Bedrock API.
About Streaming: Cohere model outputs 2 additional chunks at the end of the text being generated via streaming: a chunk containing the text `<EOS_TOKEN>`, and a chunk indicating the end of the stream. In this implementation I chose to ignore both chunks. An alternative solution could be to replace `<EOS_TOKEN>` with `\n`
Tests: manually tested that the new model work with both `llm.generate()` and `llm.stream()`.
Tested with `temperature`, `p` and `stop` parameters.
**Issue:** #11181
**Dependencies:** No new dependencies
**Tag maintainer:** @baskaryan
**Twitter handle:** mangelino
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I_kwDOIPDwls5yahQm
| 11,229
|
Add support for a variety of languages to LanguageParser
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[] | null | 32
| 2023-09-29T16:07:57
| 2024-01-08T16:10:37
| null |
NONE
| null |
### Feature request
[LanguageParser](https://github.com/langchain-ai/langchain/blob/e9b51513e9bab738c741a348ffbf0f36ce19faf6/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py#L21C7-L21C21) is a parser for Document Loaders that, given source code, splits each top-level function or class into separate documents. As stated in its documentation:
> This approach can potentially improve the accuracy of QA models over source code.
>
> Currently, the supported languages for code parsing are Python and JavaScript.
We would like to add support for additional languages, such as C, C++, Rust, Ruby, Perl, and so on.
### Motivation
There is an [open request](https://github.com/langchain-ai/langchain/issues/10996) for "CPP" (presumably C++) support. By integrating a generic parsing library (such as [tree-sitter](https://github.com/tree-sitter/py-tree-sitter#py-tree-sitter)), we could make LanguageParser work with many more languages, and thus be more generally useful.
### Your contribution
We intend to submit a pull request for this issue no later than mid-November, and likely sooner.
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I_kwDOIPDwls5yacn5
| 11,226
|
Encountering a "type object is not subscriptable" error when invoking hub.pull.
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| 2023-09-29T15:55:14
| 2023-11-17T00:41:04
| 2023-10-03T14:23:34
|
NONE
| null |
### System Info
Python = 3.8.16
langchain==0.0.304
langchainhub==0.1.13
### Who can help?
@hwchase17 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. install python 3.8.16
2. install langchain==0.0.304 and langchainhub==0.1.13
3. Run the following script :
from langchain import hub
# LANGCHAIN_HUB_API_KEY is set on my .env file
# pull a chat prompt
try:
prompt = hub.pull("efriis/my-first-prompt")
except Exception as e:
print(e)
raise e
### Expected behavior
I'm uncertain about the expected behavior, as I've never been able to successfully execute hub.pull.
|
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I_kwDOIPDwls5yabO4
| 11,225
|
DOC: What is the difference between OpenAIFunctionsAgent and OpenAIMultiFunctionsAgent?
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[] | null | 3
| 2023-09-29T15:51:08
| 2023-10-14T00:52:42
| null |
CONTRIBUTOR
| null |
### Issue with current documentation:
What is the difference between OpenAIFunctionsAgent and OpenAIMultiFunctionsAgent? The document do not clarify it.
### Idea or request for content:
_No response_
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bump 305
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I_kwDOIPDwls5yaRHA
| 11,223
|
How to disable logger streaming for async LLMChain calls?
|
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[] | null | 1
| 2023-09-29T15:26:52
| 2023-09-29T15:41:29
| 2023-09-29T15:41:29
|
NONE
| null |
When using async `LLMChain` calls, the logger prints out a ton of info statements that looks like this:
```
2023-09-29 08:24:51,038:INFO - message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=7 request_id=0cde8ecb8d402ec3db4ec674f0450ade response_code=200
```
I tried setting the logging level on the execution level in a bunch of ways, but to no avail. Do you have any recommendation on how to get rid of those logs?
|
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PR_kwDOIPDwls5bjGol
| 11,222
|
Implement RunnablePassthrough.assign(...)
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| 2023-09-29T15:22:46
| 2023-09-29T19:12:49
| 2023-09-29T19:12:49
|
COLLABORATOR
| null |
Passes through dict input and assigns additional keys
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5yZZZV
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raise ValueError(ALTERNATION_ERROR) ValueError: Error: Prompt must alternate between ' Human:' and ' Assistant:'.
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[] | null | 14
| 2023-09-29T13:18:59
| 2023-12-01T12:14:30
| null |
NONE
| null |
### System Info
while trying to use BedrockChat , it threw my error below , the updated prompt which am using after the recent changes in claude prompt is below .
prompt_template = """
Human:
You are a helpful, respectful, and honest assistant, dedicated to providing valuable and accurate information.
Guidance for answers below
Answer the question only using the in the context given below, and not with the prior knowledge.
If you don't see answer in the context just Reply "Sorry , the answer is not in the context so I don't know".
Now read this context and answer the question.
{context}
Based on the provided context above and information from the retriever source, provide a detailed answer to the below question
{question}
If the information is not available in the context , respond with "don't know."
Assistant:
"""
Error is below
raise ValueError(ALTERNATION_ERROR)
ValueError: Error: Prompt must alternate between '
Human:' and '
Assistant:'.
### 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
prompt_template = """
Human:
You are a helpful, respectful, and honest assistant, dedicated to providing valuable and accurate information.
Guidance for answers below
Answer the question only using the in the context given below, and not with the prior knowledge.
If you don't see answer in the context just Reply "Sorry , the answer is not in the context so I don't know".
Now read this context and answer the question.
{context}
Based on the provided context above and information from the retriever source, provide a detailed answer to the below question
{question}
If the information is not available in the context , respond with "don't know."
Assistant:
"""
modelId = 'anthropic.claude-v2'
# Langchain chain for invoking Endpoint
llm = BedrockChat(
model_id=modelId,
client=bedrock_client,
model_kwargs={"max_tokens_to_sample": 1000}
)
### Expected behavior
I would have expected the code to run with the prompt.
|
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| 1,919,210,329
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PR_kwDOIPDwls5biMZm
| 11,219
|
Clean warnings: replace type with isinstance and fix syntax
|
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| 2023-09-29T12:45:51
| 2023-09-29T16:44:53
| 2023-09-29T14:06:34
|
CONTRIBUTOR
| null |
Clean warnings: replace type with `isinstance` and fix on notebook syntax syntax
|
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PR_kwDOIPDwls5bh9kV
| 11,218
|
OpenAI gpt-3.5-turbo-instruct cost information
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[] | null | 2
| 2023-09-29T11:57:32
| 2023-09-29T15:44:56
| 2023-09-29T15:44:56
|
CONTRIBUTOR
| null |
Added pricing info for `gpt-3.5-turbo-instruct` for OpenAI and Azure OpenAI.
|
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Isolated python REPL based on docker containers
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| 2023-10-02T08:04:59
| null |
CONTRIBUTOR
| null |
## Description:
**This PR provides the ability to execute python code using `PythonREPLTool` tool inside a Docker container.** Isolated runs increase security by avoiding running malicious code generated by the model locally.
This is made possible by introducing a new REPL called `PythonContainerREPL`, which is a wrapper around the Docker container that runs the Python code.
## Resolving Dependency Conflicts
Some checks failed due to a lack of added docker to the dependencies. Adding `docker` and updating `poetry.lock` caused a major merge conflict. We'd like to know how to go about adding Docker as an additional dependency in this case.
We would add docker to `pyproject.toml` as follows if it weren't for the difficult to resolve merge conflict of `poetry.lock`:
```
[tool.poetry.dependencies]
python = ">=3.8.1,<4.0"
# Other dependencies...
docker = {version = "^6.1.3", optional = true}
[tool.poetry.extras]
docker = ["docker"]
Copy code
all = [
"docker",
# ... other packages ...
]
```
## Twitter handle:
@deepsense_ai
|
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PR_kwDOIPDwls5bhq10
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|
Add 'device' to GPT4All
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[] | null | 1
| 2023-09-29T10:54:15
| 2023-10-05T01:25:48
| 2023-10-04T00:37:31
|
CONTRIBUTOR
| null |
Add device to GPT4All
- **Description:** GPT4All now supports GPU. This commit adds the option to enable it.
- **Issue:** It closes https://github.com/langchain-ai/langchain/issues/10486
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PR_kwDOIPDwls5bhoB8
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|
Add RunnableGenerator
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| 2023-09-29T10:44:23
| 2023-09-29T14:21:39
| 2023-09-29T14:21:37
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5bhG8k
| 11,213
|
improved content retrieval by adjusting _clean_data function
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| 2023-09-29T09:08:52
| 2023-10-26T08:49:24
| null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** After some experimentation, I improved the _clean_data function in the ReadTheDocsLoader class. The old implementation would not constantly retrieve all of the text within the HTML document. A lot of times, the "page_content" would be empty. After searching the Discord channel for an answer, I decided to find the solution and am now pushing this implementation which will hopefully help others.,
- **Issue:** N/A,
- **Dependencies:** no new dependencies needed,
- **Tag maintainer:** @baskaryan ,
- **Twitter handle:** @istgus
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5yX1ba
| 11,212
|
Issue: How can I fetch the response from Pinecone using namespace with similarity_search
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| 2023-09-29T08:39:49
| 2023-12-30T16:01:32
| null |
NONE
| null |
### Issue you'd like to raise.
I fetch the result from pinecone using Pinecone's from_existing_index function.
My single Index contains multiple namespaces in pinecone.
Now I have 2 cases:
1. I want to get the result of particular namespace.
2. secondly I want to fetch the result from all the index's namespace.
How is it possible, Please Guide me accordingly?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5yXlkV
| 11,211
|
Issue: SQLDatabaseChain fails to run when initialised with custom read only memory
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[] | null | 8
| 2023-09-29T07:59:22
| 2023-09-29T14:32:37
| 2023-09-29T14:32:37
|
NONE
| null |
### Issue you'd like to raise.
I have custom memory with memory key as 'context' as follows which i would like to use in the underling chain as readonly memory
```
memory = ScopeContextMemory(memory_key='context')
readonlymemory = ReadOnlySharedMemory(memory_key='context', memory=memory)
```
i have the following template stored in a file
```
Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
Use the following format:
Question: "Question here"
SQLQuery: "SQL Query to run"
SQLResult: "Result of the SQLQuery"
Answer: "Final answer here"
Only use the following tables:
{table_info}
Use the following context for the query generation:
{context}
```
i have prompt initialised as follows
```
with open('prompts/sql_default_prompt.txt','r') as fp:
DEFAULT_PROMPT = PromptTemplate(input_variables=['input','table_info','dialect', 'context'],
template=fp.read())
```
NOTE: 'context' is added as one of the input variable
Ive initialised SQL Database chain as follows
```
llm=OpenAI(temperature=0)
chain = SQLDatabaseChain.from_llm(llm=llm,
db=db,
verbose=True,
use_query_checker=True,
prompt=DEFAULT_PROMPT,
return_intermediate_steps=True,
memory=memory,
top_k=3)
```
when i tried to run im getting an exception on missing input keys 'context'
```
chain("List Items under the shopping cart for Davis")
---------------------------------------------------------------------------
ValueError
169 missing_keys = set(self.input_keys).difference(inputs)
170 if missing_keys:
--> 171 raise ValueError(f"Missing some input keys: {missing_keys}")
ValueError: Missing some input keys: {'context'}
```
### Suggestion:
Ive tried looking at the documention on memory and its usage in chain , tools and agents but could not figure out what is going wrong here. any pointers on the cause is appreciated and how can i remediate the thing
|
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I_kwDOIPDwls5yXY1V
| 11,209
|
Return output from map chain in the final result of ConversationalRetrievalChain.
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[] | null | 3
| 2023-09-29T07:23:10
| 2023-10-15T00:07:41
| null |
NONE
| null |
### Feature request
Hi,
I am working on Document QA, and using ConversationalRetrievalChain, with map-reduce as chain type.
In the final result of ConversationalRetrievalChain, I want to get the individual output from each map-chain.
### Motivation
This is a separate issue, and I think there should be a parameter that we can pass, to get the output of these chains as well.
### Your contribution
Not as of now.
|
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| 1,918,600,924
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I_kwDOIPDwls5yW4bc
| 11,208
|
How do you stream a langchain response from Flask to React?
|
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[] | null | 2
| 2023-09-29T04:59:39
| 2023-12-30T16:01:38
| null |
NONE
| null |
### Issue you'd like to raise.
How do you stream a langchain response from Flask to React?
I am having issues with it. I successfully implemented it with Azure OpenAI API only but langchain doesn't give me the right implementation.
### Suggestion:
_No response_
|
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| 1,918,586,238
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PR_kwDOIPDwls5bgERc
| 11,207
|
Added Sendgrid utilitiy
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[] | null | 1
| 2023-09-29T04:35:36
| 2023-09-29T13:36:10
| null |
CONTRIBUTOR
| null |
- **Description:** added a Sendgrid utility to enable sending email. This is my first time adding a utility. I am not sure if I did the docs correctly. happy to iterate on this based on any feedback. Thank you!
- **Dependencies:** sendgrid module
- **Tag maintainer:** @hwchase17
|
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| 1,918,550,463
|
PR_kwDOIPDwls5bf8Xx
| 11,206
|
Fix typo in gradient.ipynb
|
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| 2023-09-29T03:46:33
| 2023-09-29T15:45:41
| 2023-09-29T15:45:41
|
CONTRIBUTOR
| null |
Enviroment -> Environment
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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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| 1,918,530,262
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PR_kwDOIPDwls5bf4GT
| 11,205
|
Update fireworks features
|
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| 2023-09-29T15:37:07
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|
CONTRIBUTOR
| null |
Description
* Update fireworks feature on web page
Issue - Not applicable
Dependencies - None
Tag maintainer - @baskaryan
|
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PR_kwDOIPDwls5bf1QR
| 11,204
|
Using langchain input types
|
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Input types
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PR_kwDOIPDwls5bfwdl
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Chroma batching
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CONTRIBUTOR
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- **Description:** Chroma >= 0.4.10 added support for batch sizes validation of add/upsert. This batch size is dependent on the SQLite limits of the target system and varies. In this change, for Chroma>=0.4.10 batch splitting was added as the aforementioned validation is starting to surface in the Chroma community (users using LC)
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @eyurtsev
- **Twitter handle:** t_azarov
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PR_kwDOIPDwls5bfoDF
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fix trubrics lint issue
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PR_kwDOIPDwls5bfYDs
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delete v1 tracer
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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),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5bfTNU
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Add support for project metadata in run_on_dataset
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docs: Mendable Search Improvements
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CONTRIBUTOR
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Improvements to the Mendable UI, more accurate responses, and bug fixes.
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LangServe: Relax requirements
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|
COLLABORATOR
| null |
Relax requirements
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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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:
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls5yVSaB
| 11,197
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Documents not being correctly indexed in vector database. ["redis.exceptions.ResponseError: my_docs: no such index"]
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[] | null | 3
| 2023-09-28T19:57:36
| 2023-10-24T17:51:26
| 2023-10-24T17:51:26
|
NONE
| null |
### System Info
Following the steps of indexing from [ https://python.langchain.com/docs/modules/data_connection/indexing ](url) you'll find the following error "redis.exceptions.ResponseError: my_docs: no such index".
You'll get this exception while using redis as retriever:
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The error is here:
If you look for the index you'll get (empty list or set).
This line makes impossible to save in the wanted format, and there's another thing: The index is not created for some reason.
I'll try to fix, but I'm not sure if it's possible for me at the moment, so I'm reporting this, I hope it helps.
### Expected behavior
Expected behavior inside Redis: "docs:indexname_:12ss2sadd"
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PR_kwDOIPDwls5beld6
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Background evaluator
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COLLABORATOR
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PR_kwDOIPDwls5bejcF
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Add langserve version
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COLLABORATOR
| null |
Add langserve version
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PR_kwDOIPDwls5beiKH
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Bedrock scheduled tests
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Add a streaming json parser
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COLLABORATOR
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<img width="1728" alt="Screenshot 2023-09-28 at 20 15 01" src="https://github.com/langchain-ai/langchain/assets/56902/ed0644c3-6db7-41b9-9543-e34fce46d3e5">
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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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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| 1,918,085,295
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PR_kwDOIPDwls5beYnW
| 11,192
|
Rm additional file check for scheduled tests
|
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| 2023-09-28T18:48:42
| 2023-09-28T19:33:54
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COLLABORATOR
| null |
cc @obi1kenobi Causing issues with GHA creds https://github.com/langchain-ai/langchain/actions/runs/6342767671/job/17229204508#step:7:36
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PR_kwDOIPDwls5beQwC
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Suppress warnings in interactive env that stem from tab completion
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Suppress warnings in interactive environments that can arise from users
relying on tab completion (without even using deprecated modules).
jupyter seems to filter warnings by default (at least for me), but ipython surfaces them all
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I_kwDOIPDwls5yUwar
| 11,189
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Bedrock InvokeModel failes on GA release
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[] | null | 7
| 2023-09-28T18:17:28
| 2023-10-09T12:39:46
| null |
NONE
| null |
### System Info
Lambda Python 3.11 runtime
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
As of Bedrock GA today, I get this error:
"/var/task/langchain/llms/bedrock.py", line 237, in _prepare_input_and_invoke raise ValueError(f"Error raised by bedrock service: {e}") ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: The requested operation is not recognized by the service.
### Expected behavior
It should invoke the model. I'm guessing with today's GA release of Bedrock, an underlying API call changed.
|
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PR_kwDOIPDwls5bePb1
| 11,188
|
add anthropic scheduled tests and unit tests
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PR_kwDOIPDwls5bePYn
| 11,187
|
temporarily skip embedding empty string test
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PR_kwDOIPDwls5beMSy
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LangServe: Update langchain requirement for publishing
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COLLABORATOR
| null |
Update langchain requirement for publishing
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PR_kwDOIPDwls5beLUL
| 11,185
|
Fix anthropic secret key when passed in via init
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| 2023-09-28T18:21:42
| 2023-09-28T18:21:41
|
COLLABORATOR
| null |
Fixes anthropic secret key when passed via init
https://github.com/langchain-ai/langchain/issues/11182
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PR_kwDOIPDwls5beEpO
| 11,184
|
[OpenSearch] Add Self Query Retriever Support to OpenSearch
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[] | null | 8
| 2023-09-28T17:41:23
| 2023-10-01T12:55:14
| 2023-09-28T19:36:52
|
CONTRIBUTOR
| null |
### Description
Add Self Query Retriever Support to OpenSearch
### Maintainers
@rlancemartin, @eyurtsev, @navneet1v
### Twitter Handle
@OpenSearchProj
|
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PR_kwDOIPDwls5bd92t
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Add source metadata to OutlookMessageLoader
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| 2023-09-28T17:18:35
| 2023-09-28T19:02:06
| 2023-09-28T18:58:12
|
CONTRIBUTOR
| null |
Description: Add "source" metadata to OutlookMessageLoader
This pull request adds the "source" metadata to the OutlookMessageLoader class in the load method. The "source" metadata is required when indexing with RecordManager in order to sync the index documents with a source.
Issue: None
Dependencies: None
Twitter handle: @ATelders
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I_kwDOIPDwls5yUNcR
| 11,182
|
Pydantic type conversion to SecreteStr for ChatAnthropic anthropic_api_key keyword value raises attribute error in Anthropic client
|
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| 2023-09-28T18:21:58
| 2023-09-28T18:21:57
|
NONE
| null |
### System Info
Amazon Linux, langchain version 0.0.304, Docker Image
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce:
1. Initialize ChatAnthropic model, with keyword "anthropic_api_key", as documented: https://api.python.langchain.com/en/latest/chat_models/langchain.chat_models.anthropic.ChatAnthropic.html#langchain.chat_models.anthropic.ChatAnthropic
2. Call chat model via __call__ method, e.g.:
"""
chat = ChatAnthropic(..., anthropic_api_key=os.environ["ANTHROPIC_API_KEY"])
messages = [....]
response = chat(messages)
"""
### Expected behavior
A normal response for a chat model is expected, but instead we get:
```
Traceback (most recent call last):
File "/var/task/app/src/utils/error_route.py", line 27, in custom_route_handler
return await original_route_handler(request)
File "/var/lang/lib/python3.10/site-packages/fastapi/routing.py", line 273, in app
raw_response = await run_endpoint_function(
File "/var/lang/lib/python3.10/site-packages/fastapi/routing.py", line 190, in run_endpoint_function
return await dependant.call(**values)
File "/var/task/app/app.py", line 184, in chat
reply = chat(data["question"])
File "/var/task/app/src/app_modules/chat.py", line 192, in __call__
res = self.chatbot(messages)
File "/var/lang/lib/python3.10/site-packages/langchain/chat_models/base.py", line 595, in __call__
generation = self.generate(
File "/var/lang/lib/python3.10/site-packages/langchain/chat_models/base.py", line 348, in generate
raise e
File "/var/lang/lib/python3.10/site-packages/langchain/chat_models/base.py", line 338, in generate
self._generate_with_cache(
File "/var/lang/lib/python3.10/site-packages/langchain/chat_models/base.py", line 490, in _generate_with_cache
return self._generate(
File "/var/lang/lib/python3.10/site-packages/langchain/chat_models/anthropic.py", line 184, in _generate
response = self.client.completions.create(**params)
File "/var/lang/lib/python3.10/site-packages/anthropic/_utils/_utils.py", line 250, in wrapper
return func(*args, **kwargs)
File "/var/lang/lib/python3.10/site-packages/anthropic/resources/completions.py", line 223, in create
return self._post(
File "/var/lang/lib/python3.10/site-packages/anthropic/_base_client.py", line 949, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File "/var/lang/lib/python3.10/site-packages/anthropic/_base_client.py", line 748, in request
return self._request(
File "/var/lang/lib/python3.10/site-packages/anthropic/_base_client.py", line 766, in _request
request = self._build_request(options)
File "/var/lang/lib/python3.10/site-packages/anthropic/_base_client.py", line 397, in _build_request
headers = self._build_headers(options)
File "/var/lang/lib/python3.10/site-packages/anthropic/_base_client.py", line 373, in _build_headers
headers = httpx.Headers(headers_dict)
File "/var/lang/lib/python3.10/site-packages/httpx/_models.py", line 71, in __init__
self._list = [
File "/var/lang/lib/python3.10/site-packages/httpx/_models.py", line 75, in <listcomp>
normalize_header_value(v, encoding),
File "/var/lang/lib/python3.10/site-packages/httpx/_utils.py", line 53, in normalize_header_value
return value.encode(encoding or "ascii")
AttributeError: 'SecretStr' object has no attribute 'encode'
```
Corresponding httpx code:
```
def normalize_header_value(
value: typing.Union[str, bytes], encoding: typing.Optional[str] = None
) -> bytes:
"""
Coerce str/bytes into a strictly byte-wise HTTP header value.
"""
if isinstance(value, bytes):
return value
return value.encode(encoding or "ascii")
```
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| 1,917,854,905
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I_kwDOIPDwls5yUCS5
| 11,181
|
Add support for Bedrock cohere models
|
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| 2023-09-28T16:10:03
| 2023-12-28T16:01:25
| null |
CONTRIBUTOR
| null |
### Feature request
Bedrock added support for Cohere models and the Bedrock llm code must be updated with the right prompt formatting.
The prompt must be passed as `"prompt"` field and not as the code below does as `"input"`
```
{
"modelId": "cohere.command-text-v14",
"contentType": "application/json",
"accept": "*/*",
"body": {
"prompt": "Write a LinkedIn post about starting a career in tech:",
"max_tokens": 100,
"temperature": 0.8,
"return_likelihood": "GENERATION"
}
}
```
https://github.com/langchain-ai/langchain/blob/44489e70291be95b5a95c2e0476f205202945778/libs/langchain/langchain/llms/bedrock.py#L71C1-L89C26
### Motivation
Getting support for all FM exposed by Bedrock via Langchain
### Your contribution
I can submit a PR
|
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I_kwDOIPDwls5yT4vj
| 11,180
|
WebBaseLoader interprets incorrectly the web_path parameter
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| 2023-09-28T15:48:19
| 2023-09-28T15:48:19
|
CONTRIBUTOR
| null |
### System Info
LangChain version: 0.0.304
### Who can help?
@eyurt
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.document_loaders.web_base import WebBaseLoader
loader = WebBaseLoader("https://www.google.com")
docs = loader.load()
```
Result: RequestException is raised with the message "Invalid URL 'h': No scheme supplied. Perhaps you meant https://h?"
### Expected behavior
The page contents correctly loads to documents.
|
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PR_kwDOIPDwls5bdZ5u
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Add safe pandas eval tool
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| 2023-09-28T15:35:12
| 2023-10-06T15:15:19
| null |
COLLABORATOR
| null |
- [ ] whitelist FROM clause to only allow selecting from the names of the data frames passed in or subqueries (which are themselves subject to same whitelisting)
<img width="1264" alt="Screenshot 2023-10-03 at 13 31 57" src="https://github.com/langchain-ai/langchain/assets/56902/33f460d0-353c-4d27-9f3b-9b4912f194e9">
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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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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_kwDOIPDwls5bdPc5
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|
LangServe: Add release workflow
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| 2023-09-28T15:07:56
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COLLABORATOR
| null |
Add release workflow to langserve
|
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PR_kwDOIPDwls5bdDpd
| 11,177
|
Enable creating Tools from any Runnable
|
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| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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_kwDOIPDwls5bc_QB
| 11,176
|
Expose lc_id as a classmethod
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COLLABORATOR
| null |
* Expose LC id as a class method
* User should not need to know that the last part of the id is the class name
|
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PR_kwDOIPDwls5bc8x5
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Implement better reprs for Runnables
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COLLABORATOR
| null |
```
ChatPromptTemplate(messages=[SystemMessagePromptTemplate(prompt=PromptTemplate(input_variables=[], template='You are a nice assistant.')), HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question'], template='{question}'))])
| RunnableLambda(lambda x: x)
| {
chat: FakeListChatModel(responses=["i'm a chatbot"]),
llm: FakeListLLM(responses=["i'm a textbot"])
}
```
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
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| 1,917,550,232
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PR_kwDOIPDwls5bcjSP
| 11,174
|
LangServe: Clean up init files
|
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COLLABORATOR
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Clean up init files
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I_kwDOIPDwls5yS3LG
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|
Issue: <Please write a comprehensive title after the 'Issue: ' prefix>
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| 2023-09-28T13:22:51
| 2023-12-28T16:01:27
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NONE
| null |
### Issue you'd like to raise.
full_chain = {
"source1": {"question": lambda x: x["question"]} | query_chain | db.run,
"source2": (lambda x: x['question']) | vectorstore.as_retriever(),
"question": lambda x: x['question'],
} | qa_prompt | ChatOpenAI(streaming=True, callbacks=[QueueCallback(q)], temperature=0, model_name='gpt-4')
how can I add chat history to this?
### Suggestion:
_No response_
|
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I_kwDOIPDwls5ySo_c
| 11,171
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how to add chat history to runnablesequence instance
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| 2023-09-28T12:54:18
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NONE
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### Issue you'd like to raise.
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how can I add chat history to this please?
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| 1,917,344,258
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I_kwDOIPDwls5ySFoC
| 11,169
|
norm_eps parameters in LlamaCpp
|
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[] | null | 2
| 2023-09-28T11:48:48
| 2023-12-28T16:01:29
| null |
NONE
| null |
### Feature request
When I use llama cpp for my case, I have [problems](https://github.com/ggerganov/llama.cpp/issues/2619). I've tried use "-esp 1e-5". It works well for me.
But when I use ```from langchain.llms import LlamaCpp``` I get the same problems. However I dont know the way to pass parameters
Thank you~~
### Motivation
Relevant with encode - decode
### Your contribution
[Llama cpp](https://github.com/ggerganov/llama.cpp)
|
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I_kwDOIPDwls5yR_mh
| 11,168
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Issue: Pinecone.from_documents cannot run on AWS Lambda because of multiprocessing
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| 2023-12-11T18:24:31
| null |
NONE
| null |
### Issue you'd like to raise.
I am writing a few apps that use LangChain and deploying them to AWS Lambda. My previous 2 lambdas are running smoothly, but the one I'm trying to deploy this week is giving me the following stack trace when the lambda is run:
``` "errorMessage": "[Errno 38] Function not implemented",
"errorType": "OSError",
"requestId": "23ef7a93-3d00-475c-b495-421810f93d92",
"stackTrace": [
" File \"/var/task/datadog_lambda/wrapper.py\", line 214, in __call__\n self.response = self.func(event, context, **kwargs)\n",
" File \"/var/task/app.py\", line 4, in lambda_handler\n total_indexed_pages = get_latest_changes()\n",
" File \"/var/task/ddtrace/contrib/aws_lambda/patch.py\", line 115, in __call__\n self.response = self.func(*args, **kwargs)\n",
" File \"/var/task/app.py\", line 4, in lambda_handler\n total_indexed_pages = get_latest_changes()\n",
" File \"/var/task/index_latest_docs.py\", line 137, in get_latest_changes\n raise e\n",
" File \"/var/task/index_latest_docs.py\", line 126, in get_latest_changes\n Pinecone.from_documents(docs, embedding=embeddings, index_name=PINECONE_INDEX_NAME, namespace=PINECONE_NAMESPACE)\n",
" File \"/var/task/langchain/schema/vectorstore.py\", line 420, in from_documents\n return cls.from_texts(texts, embedding, metadatas=metadatas, **kwargs)\n",
" File \"/var/task/langchain/vectorstores/pinecone.py\", line 416, in from_texts\n pinecone.add_texts(\n",
" File \"/var/task/langchain/vectorstores/pinecone.py\", line 138, in add_texts\n async_res = [\n",
" File \"/var/task/langchain/vectorstores/pinecone.py\", line 139, in <listcomp>\n self._index.upsert(\n",
" File \"/var/task/pinecone/core/utils/error_handling.py\", line 17, in inner_func\n return func(*args, **kwargs)\n",
" File \"/var/task/pinecone/index.py\", line 150, in upsert\n return self._upsert_batch(vectors, namespace, _check_type, **kwargs)\n",
" File \"/var/task/pinecone/index.py\", line 237, in _upsert_batch\n return self._vector_api.upsert(\n",
" File \"/var/task/pinecone/core/client/api_client.py\", line 776, in __call__\n return self.callable(self, *args, **kwargs)\n",
" File \"/var/task/pinecone/core/client/api/vector_operations_api.py\", line 956, in __upsert\n return self.call_with_http_info(**kwargs)\n",
" File \"/var/task/pinecone/core/client/api_client.py\", line 838, in call_with_http_info\n return self.api_client.call_api(\n",
" File \"/var/task/pinecone/core/client/api_client.py\", line 421, in call_api\n return self.pool.apply_async(self.__call_api, (resource_path,\n",
" File \"/var/task/pinecone/core/client/api_client.py\", line 107, in pool\n self._pool = ThreadPool(self.pool_threads)\n",
" File \"/var/lang/lib/python3.11/multiprocessing/pool.py\", line 930, in __init__\n Pool.__init__(self, processes, initializer, initargs)\n",
" File \"/var/lang/lib/python3.11/multiprocessing/pool.py\", line 196, in __init__\n self._change_notifier = self._ctx.SimpleQueue()\n",
" File \"/var/lang/lib/python3.11/multiprocessing/context.py\", line 113, in SimpleQueue\n return SimpleQueue(ctx=self.get_context())\n",
" File \"/var/lang/lib/python3.11/multiprocessing/queues.py\", line 341, in __init__\n self._rlock = ctx.Lock()\n",
" File \"/var/lang/lib/python3.11/multiprocessing/context.py\", line 68, in Lock\n return Lock(ctx=self.get_context())\n",
" File \"/var/lang/lib/python3.11/multiprocessing/synchronize.py\", line 167, in __init__\n SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)\n",
" File \"/var/lang/lib/python3.11/multiprocessing/synchronize.py\", line 57, in __init__\n sl = self._semlock = _multiprocessing.SemLock
```
On line 126 in my index_latest_docs.py file, I have the following line:
`Pinecone.from_documents(docs, embedding=embeddings, index_name=PINECONE_INDEX_NAME, namespace=PINECONE_NAMESPACE)`
This is causing it to not run in AWS Lambda because Lambda cannot handle multiprocessing, which the function seems to be doing internally.
I don't know if this is a newly introduced issue, because I'm using the same exact function in my other lambda apps, and they seem to be running fine - but maybe the containers were built with a different version than this more recent one.
Any help would be greatly appreciated
### Suggestion:
_No response_
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| 1,917,278,742
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I_kwDOIPDwls5yR1oW
| 11,167
|
[BUG] OutputParserException when using CSV agent as a tool for another agent
|
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[] | null | 5
| 2023-09-28T11:15:57
| 2023-10-05T13:33:50
| 2023-10-05T13:33:50
|
NONE
| null |
### System Info
langchain==0.0.299
Python 3.10.13
OS: Windows 10
### 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
- [x] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.agents import initialize_agent
from langchain.agents.agent_types import AgentType
from langchain.chat_models import AzureChatOpenAI
from langchain.agents.tools import Tool
from langchain.agents import load_tools
from langchain.agents import create_csv_agent
from langchain.memory import ConversationBufferMemory
llm = AzureChatOpenAI(
model="gpt-4",
temperature=0,
openai_api_version="2023-05-15",
verbose=True,
)
tools = load_tools(["wikipedia", "llm-math"], llm=llm)
csv_agent = create_csv_agent(
llm=llm,
path=["sample_02.csv"], # some sales data
verbose=True,
handle_parsing_errors=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
memory=ConversationBufferMemory(),
)
csv_tool = Tool(
name="CSV Agent",
func=csv_agent.run,
description="Useful for interacting with CSV data.",
)
tools.append(csv_tool)
agent = initialize_agent(
llm=llm,
tools=tools,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
handle_parsing_errors=True,
memory=ConversationBufferMemory(),
)
agent.run("What is the sum of sales")
```
### Expected behavior
I am getting exception:
```
File "C:\Users\X\lib\site-packages\langchain\agents\mrkl\output_parser.py", line 52, in parse raise OutputParserException( langchain.schema.output_parser.OutputParserException: Could not parse LLM output: 'some info about data comes here'
```
Expected: No exception and agent should have same behaviour as if I would use the CSV agent standalone
|
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| 1,917,264,832
|
I_kwDOIPDwls5yRyPA
| 11,166
|
Runtime Exception in create_extraction_chain_pydantic (RuntimeError: no validator found)
|
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open
| false
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[] | null | 6
| 2023-09-28T11:09:25
| 2023-12-30T16:02:21
| null |
NONE
| null |
### System Info
Langchain 0.0.302
Pydantic 2.4.1
reproducing code:
```
from langchain.chains import create_extraction_chain_pydantic
from langchain.chat_models import ChatOpenAI
from pydantic import BaseModel, model_validator
from typing import Any
class TestData(BaseModel):
company_name: str
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
chain = create_extraction_chain_pydantic(pydantic_schema=TestData, llm=llm)
extracted_subsidiary_list = chain.run('')
```
Exception:
**RuntimeError: no validator found for <class '__main__.TestData'>, see `arbitrary_types_allowed` in Config**
Tried adding: (didn't help)
```
@model_validator(mode='after')
def dummy_validator(self):
return self
@model_validator(mode='before')
@classmethod
def dummy_validator1(cls, data: Any) -> Any:
return data
```
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.chains import create_extraction_chain_pydantic
from langchain.chat_models import ChatOpenAI
from pydantic import BaseModel, model_validator
from typing import Any
class TestData(BaseModel):
company_name: str
llm = ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
chain = create_extraction_chain_pydantic(pydantic_schema=TestData, llm=llm)
extracted_subsidiary_list = chain.run('')
```
### Expected behavior
Doesn't throw exception
|
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| 1,917,252,474
|
I_kwDOIPDwls5yRvN6
| 11,165
|
Argument 'value' has incorrect type (expected str, got SecretStr) (type=type_error) in chatAnthropic- Induced in 0.0.301
|
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| false
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[] | null | 4
| 2023-09-28T11:04:07
| 2023-10-03T12:20:10
| 2023-10-03T12:20:10
|
NONE
| null |
### System Info
Hi Team,
The issue is while calling the ChatAnthropic function with the right anthropic secret key. The type SecretStr is not getting resolved. Discussed over here:https://github.com/langchain-ai/langchain/pull/10724
### Who can help?
@josh
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
To recreate the error :
from langchain.chat_models import ChatAnthropic
claude_chat = ChatAnthropic(anthropic_api_key="xy-key")
### Expected behavior
It should initialize the claude chat
|
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PR_kwDOIPDwls5bbaJL
| 11,164
|
add baidu elasticsearch vectorstore
|
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| 2023-09-28T10:51:46
| 2023-10-10T14:07:24
| 2023-10-10T14:07:06
|
CONTRIBUTOR
| null |
**Description:**
add baidu elasticsearch vectorstore
|
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| 1,917,192,653
|
I_kwDOIPDwls5yRgnN
| 11,163
|
Add `hierarchy` key to Document metadata in `ConfluenceLoader`
|
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open
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[] | null | 2
| 2023-09-28T10:33:12
| 2023-12-28T16:01:12
| null |
NONE
| null |
### Feature request
Add `hierarchy` key to Document metadata returned by `ConfluenceLoader`.
So, for example if a page is located under the pages Page A/Page B, the `hierarchy` should be equal to `Page A/Page B`
This could be achieved by calling the confluence endpoint using `expand = "ancestors"`.
For example [this](https://github.com/langchain-ai/langchain/blob/cfa2203c626a2287d60c1febeb3e3a68b77acd77/libs/langchain/langchain/document_loaders/confluence.py#L282) would become
```python
pages = self.paginate_request(
self.confluence.get_all_pages_from_space,
space=space_key,
limit=limit,
max_pages=max_pages,
status="any" if include_archived_content else "current",
expand=f"{content_format.value},ancestors",
)
```
### Motivation
Page Hierarchy is very important in a confluence-like space and it would definitely be relevant when searching for documents in the Vector Store.
### Your contribution
I am willing to submit a PR to add this feature.
|
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PR_kwDOIPDwls5bbLkh
| 11,161
|
Update Bedrock service name to "bedrock-runtime" and model identifiers
|
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| 2023-09-28T16:43:09
| 2023-09-28T16:42:57
|
CONTRIBUTOR
| null |
- **Description:** Bedrock updated boto service name to "bedrock-runtime" for the InvokeModel and InvokeModelWithResponseStream APIs. This update also includes new model identifiers for Titan text, embedding and Anthropic.
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chore: add support for TypeScript code splitting
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. 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 typescript language to `TextSplitter`
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I_kwDOIPDwls5yRGA4
| 11,159
|
Raise a warning when using OpenAI and Azure OpenAI Completion API
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| 2023-09-28T09:27:24
| 2023-12-28T16:01:07
| null |
CONTRIBUTOR
| null |
### Feature request
It looks like most of the models of the `Completion` API have been marked as legacy and will be discontinued in early 2024.
On Azure OpenAI I already no longer have access to non-chat models and I can't use the completion API with the newest get-35-turbo and gpt4 (0613).
Shall we raise a warning to the user when instantiating `langchain.llms.OpenAI` and `langchain.llms.AzureOpenAI` with deprecated models?
References:
- https://openai.com/blog/gpt-4-api-general-availability
- https://platform.openai.com/docs/deprecations
### Your contribution
I can contribute to this if the feature makes sense.
|
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PR_kwDOIPDwls5bakuN
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[ElasticsearchStore] Improve migration text to ElasticsearchStore
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| 2023-09-28T19:40:19
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CONTRIBUTOR
| null |
We noticed that as we have been moving developers to the new `ElasticsearchStore` implementation, we want to keep the ElasticVectorSearch class still available as developers transition slowly to the new store.
To speed up this process, I updated the blurb giving them a better recommendation of why they should use ElasticsearchStore.
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ISSUE: how can i get answer only based on vector store? If the context is not available in vector store
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open
| false
| null |
[] | null | 8
| 2023-09-28T08:16:34
| 2023-10-21T14:45:00
| null |
NONE
| null |
template = """Use the following pieces of context to answer the question at the end. If the answer can't be determined using only the information in the provided context simply output "NO ANSWER", just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
qa = RetrievalQA.from_chain_type(
llm=streaming_llm,
chain_type="stuff",
retriever=compression_retriever,
callback_manager=manager,
chain_type_kwargs={
"prompt": QA_CHAIN_PROMPT
}
)
|
{
"url": "https://api.github.com/repos/langchain-ai/langchain/issues/11157/reactions",
"total_count": 0,
"+1": 0,
"-1": 0,
"laugh": 0,
"hooray": 0,
"confused": 0,
"heart": 0,
"rocket": 0,
"eyes": 0
}
|
https://api.github.com/repos/langchain-ai/langchain/issues/11157/timeline
| null | null | null | null |
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