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https://api.github.com/repos/langchain-ai/langchain/issues/15790 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15790/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15790/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15790/events | https://github.com/langchain-ai/langchain/issues/15790 | 2,073,482,807 | I_kwDOIPDwls57ltY3 | 15,790 | create_structured_output_chain doesn't invoke the given callback and on_llm_new_token with tokens | {
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] | open | false | null | [] | null | 2 | 2024-01-10T02:43:26 | 2024-01-11T21:12:06 | null | NONE | null | ### System Info
```
end_response = chain.run(
input=input["input"],
question=input["question"],
callbacks=[StreamingHandler()],
tags=tags,
)
```
```
StreamingHandler() is an extension of the langchain class `BaseCallbackHandler` and extends its methods:
```
def on_llm_new_token(self, token: str, **kwargs) -> None:
if token:
self.queue_event(event_data=token)
```
With a regular `LLMChain`:
```
conv_chain = LLMChain(
llm=llm,
memory=memory,
prompt=chain_prompt,
verbose=True,
)
```
this `on_llm_new_token` method gets invoked each call with each new token.
However, with create_structured_output_chain, it seems to get invoked with empty tokens each time:
```
conv_chain = create_structured_output_chain(
output_schema=APydanticClass,
llm=llm,
prompt=chain_prompt,
verbose=True,
)
```
### Who can help?
@agola11 seems the right perrson to tag 🙏
### Information
- [X] The official example notebooks/scripts
- [ ] 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Use create_structured_output_chain with a pydantic schema
2. Attach a callback with on_llm_new_token overriden
3. on_llm_new_token gets invoked with empty tokens.
### Expected behavior
Tokens streamed back in the json format of the schema requested. E.g. if the schema is:
```
class XYZ(BaseModel):
matches: Optional[List[str]] = Field(
default=None, description="abc"
)
not_matches: Optional[List[str]] = Field(
default=None,
description="def",
)
```
I'd expect it to be streamed back token by token or even category by category. | {
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- **Issue:** #15664
- **Dependencies:** N/A | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15788 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15788/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15788/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15788/events | https://github.com/langchain-ai/langchain/pull/15788 | 2,073,414,709 | PR_kwDOIPDwls5joktV | 15,788 | community: add collection_properties parameter to Milvus | {
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- **Issue:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/15787 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15787/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15787/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15787/events | https://github.com/langchain-ai/langchain/pull/15787 | 2,073,374,646 | PR_kwDOIPDwls5jocE_ | 15,787 | mistralai[patch]: warn on stop token, fix on_llm_new_token | {
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Addresses with warning. MistralAI API doesn't support stop token yet. | {
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] | open | false | null | [] | null | 6 | 2024-01-10T00:05:52 | 2024-01-17T11:25:33 | null | NONE | null | ### System Info
langchain==0.0.352
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.retrievers import (
GoogleVertexAIMultiTurnSearchRetriever,
GoogleVertexAISearchRetriever,
GoogleCloudEnterpriseSearchRetriever
)
PROJECT_ID = "my_project_id"
SEARCH_ENGINE_ID = "I tried both for datastore_id and app_id at Vertex Search"
LOCATION_ID = "us"
retriever = GoogleCloudEnterpriseSearchRetriever(
project_id=PROJECT_ID,
search_engine_id=SEARCH_ENGINE_ID,
location_id=LOCATION_ID,
max_documents=3
)
while 1:
message = input()
result = retriever.get_relevant_documents(message)
for doc in result:
print(doc)
```
### Expected behavior
I expected it works well with the defined datastore, but it returned the error saying,
```
google.api_core.exceptions.NotFound: 404 DataStore projects/500618827687/locations/us/collections/default_collection/dataStores/['datastore_id'] not found
``` | {
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- update test model
- cr
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- [x] release core
- [x] bump min core version for community | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15779 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15779/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15779/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15779/events | https://github.com/langchain-ai/langchain/issues/15779 | 2,073,178,041 | I_kwDOIPDwls57ki-5 | 15,779 | Issue with LangChain v0.0.335 - Error in ChatOpenAI Callbacks Expected Runnable Instances | {
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] | closed | false | null | [] | null | 3 | 2024-01-09T21:13:36 | 2024-01-10T18:32:48 | 2024-01-10T18:32:48 | NONE | null | ### Issue you'd like to raise.
I am currently utilizing LangChain version 0.0.335 in my Fast API Python application. In the main.py file, the following code snippet is implemented:
main.py
```
streaming_model = ChatOpenAI(
model_name="gpt-4",
temperature=0.1,
openai_api_key=os.getenv("OPENAI_API_KEY2"),
)
non_streaming_model = ChatOpenAI(
model_name="gpt-4",
temperature=0.1,
openai_api_key=os.getenv("OPENAI_API_KEY2"),
)
retriever = vector_store.as_retriever()
sales_persona_prompt = PromptTemplate.from_template(SALES_PERSONA_PROMPT)
condense_prompt = PromptTemplate.from_template(CONDENSE_PROMPT)
chain = ConversationalRetrievalChain.from_llm(
llm=streaming_model,
retriever=retriever,
condense_question_prompt=condense_prompt,
condense_question_llm=non_streaming_model,
combine_docs_chain_kwargs={"prompt": sales_persona_prompt},
verbose=True,
)
return chain(
{"question": sanitized_question, "chat_history": conversation_history}
)
except Exception as e:
return {"error": str(e)}
```
However, this implementation throws the following error:
` "error": "2 validation errors for LLMChain\nllm\n instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)\nllm\n instance of Runnable expected (type=type_error.arbitrary_type; expected_arbitrary_type=Runnable)"`
Expected Behavior:
I expected the code to execute without errors. The issue seems to be related to the expected types for the llm parameter in the ConversationalRetrievalChain.from_llm method.
Request for Assistance:
I kindly request assistance in understanding and resolving this issue. Any insights, recommendations, or specific steps to address the error would be highly appreciated.
Thank you for your time and support.
### Suggestion:
_No response_ | {
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<img width="282" alt="Screenshot 2024-01-09 at 1 08 53 PM" src="https://github.com/langchain-ai/langchain/assets/6952323/58f3d368-6588-418e-8502-30d13757cb99">
CC @efriis @baskaryan
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- Revert "Hardwire release actions"
- Initial implementation
- Initial tests update
- Test fixes
- Remove redundant description
- Initial docs update
- - Add missing Langsmith dependency - Update docs
- Add template
- Fix linting
- remove code duplicate use urljoin to ensure openapi path
- lint
- docs/toolkit update
- fix typo and test..
- Update README
- fix case where action has no doc string
- refactoring
- Conditionally return structured/unstructured tools
- Update docs
- Fix formatting
- Leave template only
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- [ ] update deps/lockfile to reflect published version | {
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- google
- some pip openai
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- fmt
- docs: google vertex partner docs
- fmt
- docs: more pip installs
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The code currently uses the expression `self.mode == ["page"]` to check which mode the user has selected. In Python, this comparison is always `False` (self.mode is asserted to be str in line 35), thus the if-statement would always result in the execution of `_generate_docs_object` instead of `_generate_docs_page`.
The fix is to remove the brackets resulting in a correct comparison.
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**Platform**: Ubuntu 22.04
**Python**: 3.10
**Langchain**:
langchain 0.1.0
langchain-community 0.0.10
langchain-core 0.1.8
langchain-openai 0.0.2
langsmith 0.0.78
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I used this code :
```py
out = chain.batch(entries, config={"max_concurrency": 3})
```
I can see in Langsmith that more than 12 requests were made at parallel, causing rate limit failure with OpenAI API (TPM).
```
RateLimitError: Error code: 429 - {'error': {'message': 'Rate limit reached for gpt-3.5-turbo-1106 in organization org-W83OoPhCAmgMx2r35aLyv9Tr on tokens per min (TPM): Limit 60000, Used 54134, Requested 6465. Please try again in 599ms. Visit https://platform.openai.com/account/rate-limits to learn more.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}
```
### Expected behavior
I would expect max_concurrency to limit the amount of concurrency used, but actually that doesn't seem to be the case.
Batch doesn't seem to limit concurrency at all.
This code works perfectly :
```py
from concurrent.futures import ThreadPoolExecutor
def batch_chain(inputs: list) -> list:
with ThreadPoolExecutor(max_workers=3) as executor:
return list(executor.map(chain.invoke, inputs))
out = batch_chain(entries)
``` | {
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] | null | 1 | 2024-01-09T18:15:13 | 2024-01-17T19:25:23 | null | CONTRIBUTOR | null | I also added LANGCHAIN_COMET_TRACING to enable the CometLLM tracing integration similar to other tracing integrations. This is easier for end-users to enable it rather than importing the callback and pass it manually.
(This is the same content as https://github.com/langchain-ai/langchain/pull/14650 but rebased and squashed as something seems to confuse Github Action).
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How can EnsembleRetriever be called asynchronously? I have a dataset with ~1k questions and I wish to find the documents that can best answer each of them. However, calling it sequentially takes a lot of time. Can I run the retriever in parallel for all rows (or chunks of it)? Or is there a different way to optimise the run times?
I'm calling it like this now but it gives out a segmentation fault after getting stuck for an hour
```
import asyncio
queries = [query1, query2, ...]
async def process_query(profile):
result = await ensemble_retriever.aget_relevant_documents(profile)
return result
async def process_all_queries():
tasks = [process_query(query) for query in queries]
results = await asyncio.gather(*tasks)
return results
results = asyncio.run(process_all_queries())
```
### Suggestion:
_No response_ | {
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Still occurrences that need changing - will update more later | {
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- **Dependencies:** None | {
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- **Dependencies:** This template depends on the AI playground service in NVIDIA NGC. API keys with a significant trial compute are available (10k queries at the time of writing). This template also depends on the Milvus Vector store which is publicly available.
Note: [A quick link to get a key](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/codellama-13b/api) when you have an NGC account. Generate Key button at the top right of the code window. | {
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- **Issue:** [15664](https://github.com/langchain-ai/langchain/issues/15664)
- **Dependencies:** None
- **Twitter handle:** @davedecaprio
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I am developing a Streamlit application where I aim to stream the agent's responses to the UI. Previously, I was able to achieve this by utilizing chains with a simple call to ```chain.stream()```. However, after switching to agents, I cannot stream its response in the same way given that it is implemented in LCEL.
I've tried to use ```StreamingStdOutCallbackHandler``` but the response gets streamed in the terminal only and not to the UI.
Any insights, guidance, or fixes regarding this issue would be greatly appreciated
### Suggestion:
_No response_ | {
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] | null | 3 | 2024-01-09T12:50:51 | 2024-01-09T18:32:38 | 2024-01-09T18:32:36 | CONTRIBUTOR | null | **Description:**
Adds the initial implementation of the [Robocorp Action Server](https://github.com/robocorp/robo/tree/master/action_server/docs) integration as Langchain Agent toolkit. | {
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] | null | 1 | 2024-01-09T11:41:15 | 2024-01-15T19:03:24 | null | NONE | null | Description: Added the parameter for a possibility to change a language model in SpacyEmbeddings. The default value is still the same: "en_core_web_sm", so it shouldn't affect a code which previously did not specify this parameter, but it is not hard-coded anymore and easy to change in case you want to use it with other languages or models.
Issue: At Barcelona Supercomputing Center in Aina project (https://github.com/projecte-aina), a project for Catalan Language Models and Resources, we would like to use Langchain for one of our current projects and we would like to comment that Langchain, while being a very powerful and useful open-source tool, is pretty much focused on English language. We would like to contribute to make it a bit more adaptable for using with other languages.
Dependencies: This change requires the Spacy library and a language model, specified in the model parameter.
Tag maintainer: @dev2049
Twitter handle: @projecte_aina
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Everything was working fine but now suddenly I'm receiving all sorts of LangChain Deprecation issues.
I installed the langchain_openai package and also installed langchain_community package too and replaced all the imports with the suggested ones in the error. It went well but now I'm stuck at this issue
The error is:
`/home/ec2-user/anaconda3/envs/JupyterSystemEnv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:115: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(`
In my code I've replaced all "run" to "invoke" but don't know why this warning is coming up.
I'm also using a LangChain Summarizer and I checked the documentation and it's exactly the way it is in the documentation.
I don't know how to get rid of that deprecation warning now. I don't want to suppress the warning, I want to resolve it so it won't cause any issue in the future.
**This is the only code that I've related to LangChain:**
```
# Langchain Libraries
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.docstore.document import Document
from langchain_community.callbacks import get_openai_callback
from langchain.text_splitter import TokenTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain_core.output_parsers import StrOutputParser
# ------------------------------------------------------------
# General ChatGPT function that's required for all the Call-type Prompts
def chatgpt_function(prompt, transcript):
model_kwargs={"seed":235, "top_p":0.01}
llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0, model_kwargs=model_kwargs, max_tokens=tokens)
template = """
{prompt}
Call Transcript: ```{text}```
"""
prompt_main = PromptTemplate(
input_variables=["prompt", "text"],
template=template,)
with get_openai_callback() as cb:
# llm_chain = LLMChain(llm=llm, prompt=prompt_main)
output_parser = StrOutputParser()
llm_chain = prompt_main | llm | output_parser
all_text = str(template) + str(prompt) + str(transcript)
threshold = (llm.get_num_tokens(text=all_text) + tokens)
# print("Total Tokens:",threshold)
if int(threshold) <= 4000:
chatgpt_output = llm_chain.invoke({"prompt":prompt, "text":transcript})
else:
transcript_ = token_limiter(transcript)
chatgpt_output = llm_chain.invoke({"prompt":prompt, "text":transcript_})
return chatgpt_output
# -------------------------------------------------------
# Function to get refined summary if Transcript is long
def token_limiter(transcript):
text_splitter = TokenTextSplitter(chunk_size=3000, chunk_overlap=200)
texts = text_splitter.split_text(transcript)
docs = [Document(page_content=text) for text in texts]
question_prompt_template = """
I'm providing you a call transcript refined summary enclosed in triple backticks. summarize it furter.
Call Transcript: ```{text}```
Provide me a summary transcript. do not add add any title/ heading like summary or anything else. just give summary text.
"""
question_prompt = PromptTemplate(
template=question_prompt_template, input_variables=["text"]
)
refine_prompt_template = """
Write a summary of the following text enclosed in triple backticks (```).
```{text}```
"""
refine_prompt = PromptTemplate(
template=refine_prompt_template, input_variables=["text"]
)
llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0, max_tokens=800)
refine_chain = load_summarize_chain(
llm,
chain_type="refine",
question_prompt=question_prompt,
refine_prompt=refine_prompt,
return_intermediate_steps=True,
)
summary_refine = refine_chain({"input_documents": docs}, return_only_outputs=True)
return summary_refine['output_text']```
### Suggestion:
Please let me know what I need to change in my code to get rid of that Deprecation warning. Thank you | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15740 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15740/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15740/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15740/events | https://github.com/langchain-ai/langchain/pull/15740 | 2,072,108,915 | PR_kwDOIPDwls5jkF3K | 15,740 | community: Support milvus partition key. | {
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- **Description:** Milvus's partition key is an important feature. It can support multi-tenancy. We hope to introduce this feature. https://milvus.io/docs/partition_key.md
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** No
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https://api.github.com/repos/langchain-ai/langchain/issues/15739 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15739/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15739/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15739/events | https://github.com/langchain-ai/langchain/issues/15739 | 2,072,094,761 | I_kwDOIPDwls57gagp | 15,739 | Connection error caused failure to post http://localhost:1984/runs in LangSmith API. | {
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] | open | false | null | [] | null | 2 | 2024-01-09T10:36:08 | 2024-01-18T03:16:56 | null | NONE | null | ### System Info
From pyproject.toml:
python=3.11.5
crewai = "0.1.6"
langchain = '==0.0.335'
openai = '==0.28.1'
unstructured = '==0.10.25'
pyowm = '3.3.0'
tools = "^0.1.9"
wikipedia = "1.4.0"
yfinance = "0.2.33"
sec-api = "1.0.17"
tiktoken = "0.5.2"
faiss-cpu = "1.7.4"
python-dotenv = "1.0.0"
### 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
Running any of the scripts in the crewAI (https://github.com/joaomdmoura/crewAI)
Running crewAI I get the following.
Connection error caused failure to patch http://localhost:1984/runs/7fdd9cf2-4f50-4ee1-8fef-9202b07cc756 in LangSmith API. Please confirm your LANGCHAIN_ENDPOINT. ConnectionError(MaxRetryError("HTTPConnectionPool(host='localhost', port=1984): Max retries exceeded with url: /runs/7fdd9cf2-4f50-4ee1-8fef-9202b07cc756 (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x130d51e10>: Failed to establish a new connection: [Errno 61] Connection refused'))"))
Connection error caused failure to post http://localhost:1984/runs in LangSmith API. Please confirm your LANGCHAIN_ENDPOINT. ConnectionError(MaxRetryError("HTTPConnectionPool(host='localhost', port=1984): Max retries exceeded with url: /runs (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x130d6b390>: Failed to establish a new connection: [Errno 61] Connection refused'))"))
I ma not running LangSmith nor do I have any access to it. I have tried setting in my .env to no effect.
LANGCHAIN_TRACING=false
LANGCHAIN_TRACING_V2=false
LANGCHAIN_HANDLER=None
### Expected behavior
Dont expect to see the error reports. Note that not all users are seeing this error | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15738 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15738/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15738/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15738/events | https://github.com/langchain-ai/langchain/pull/15738 | 2,072,077,270 | PR_kwDOIPDwls5jj--6 | 15,738 | Add AstraDB self query retriever | {
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- **Twitter handle:** cbornet_
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https://api.github.com/repos/langchain-ai/langchain/issues/15737 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15737/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15737/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15737/events | https://github.com/langchain-ai/langchain/pull/15737 | 2,071,942,645 | PR_kwDOIPDwls5jjh56 | 15,737 | community: Added functions to make async calls to HuggingFaceHub's embedding endpoint in HuggingFaceHubEmbeddings class | {
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] | closed | false | null | [] | null | 1 | 2024-01-09T09:14:51 | 2024-01-12T05:52:56 | 2024-01-12T05:52:55 | CONTRIBUTOR | null | **Description:**
Added aembed_documents() and aembed_query() async functions in HuggingFaceHubEmbeddings class in
langchain_community\embeddings\huggingface_hub.py file. It will support to make async calls to HuggingFaceHub's
embedding endpoint and generate embeddings asynchronously.
Test Cases: Added test_huggingfacehub_embedding_async_documents() and test_huggingfacehub_embedding_async_query()
functions in test_huggingface_hub.py file to test the two async functions created in HuggingFaceHubEmbeddings class.
Documentation: Updated huggingfacehub.ipynb with steps to install huggingface_hub package and use
HuggingFaceHubEmbeddings.
**Dependencies:** None,
**Twitter handle:** I do not have a Twitter account
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] | closed | false | null | [] | null | 2 | 2024-01-09T09:01:44 | 2024-01-09T09:10:19 | 2024-01-09T09:09:28 | CONTRIBUTOR | null | - **Description:**
Added aembed_documents() and aembed_query() async functions in HuggingFaceHubEmbeddings class in
langchain_community\embeddings\huggingface_hub.py file. It will support to make async calls to HuggingFaceHub's
embedding endpoint and generate embeddings asynchronously.
Test Cases: Added test_huggingfacehub_embedding_async_documents() and test_huggingfacehub_embedding_async_query()
functions in test_huggingface_hub.py file to test the async functions created in HuggingFaceHubEmbeddings class.
Documentation: Updated huggingfacehub.ipynb with steps to install _huggingface_hub_ package and use
HuggingFaceHubEmbeddings.
- **Dependencies:** None,
- **Twitter handle:** I do not have a Twitter account
| {
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https://api.github.com/repos/langchain-ai/langchain/issues/15735 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15735/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15735/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15735/events | https://github.com/langchain-ai/langchain/issues/15735 | 2,071,916,307 | I_kwDOIPDwls57fu8T | 15,735 | How can I specify my own designated table for vector search to retrieve vector data for comparison and provide a response to OpenAI for reference? Also, I noticed the prompt disappeared | {
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] | open | false | null | [] | null | 2 | 2024-01-09T08:59:07 | 2024-01-09T09:01:18 | null | NONE | null | ### Issue you'd like to raise.
import os
from urllib.parse import quote_plus
from langchain.vectorstores.pgvector import PGVector
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder,
HumanMessagePromptTemplate
os.environ['OPENAI_API_KEY'] = "key"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host="1x2.1x8.xx.xx",
port=5432,
database="Ai",
user="xxxxxxxxx",
password=quote_plus("xxxxxx@xx"),
)
vectordb = PGVector(embedding_function=embeddings,
collection_name="tmp04",
connection_string=CONNECTION_STRING,
)
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"i am robot"
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
retriever = vectordb.as_retriever()
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 1000
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True,
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({})
question = input('ask：')
# question = retriever.get_relevant_documents(input('ask：'))
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
vectordb.add_vector(res)
select_vdb = vectordb.similarity_search(question, k=5)
print(select_vdb)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
![Uploading image.png…]()
### Suggestion:
How can I specify my own designated table for vector search to retrieve vector data for comparison and provide a response to OpenAI for reference? Also, I noticed the prompt disappeared | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15734 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15734/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15734/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15734/events | https://github.com/langchain-ai/langchain/issues/15734 | 2,071,885,054 | I_kwDOIPDwls57fnT- | 15,734 | How can I specify my own designated table for vector search to retrieve vector data for comparison and provide a response to OpenAI for reference? Also, I noticed the prompt disappeared. | {
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}
] | closed | false | null | [] | null | 4 | 2024-01-09T08:39:38 | 2024-01-09T08:48:55 | 2024-01-09T08:48:01 | NONE | null | ### Issue with current documentation:
import os
from urllib.parse import quote_plus
from langchain.vectorstores.pgvector import PGVector
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder,
HumanMessagePromptTemplate
os.environ['OPENAI_API_KEY'] = "key"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host="1x2.1x8.xx.xx",
port=5432,
database="Ai",
user="xxxxxxxxx",
password=quote_plus("xxxxxx@xx"),
)
vectordb = PGVector(embedding_function=embeddings,
collection_name="tmp04",
connection_string=CONNECTION_STRING,
)
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"i am robot"
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
retriever = vectordb.as_retriever()
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 1000
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True,
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({})
question = input('ask：')
# question = retriever.get_relevant_documents(input('提問：'))
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
vectordb.add_vector(res)
select_vdb = vectordb.similarity_search(question, k=5)
print(select_vdb)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
### Idea or request for content:
How can I specify my own designated table for vector search to retrieve vector data for comparison and provide a response to OpenAI for reference? Also, I noticed the prompt disappeared. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15733 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15733/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15733/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15733/events | https://github.com/langchain-ai/langchain/issues/15733 | 2,071,858,324 | I_kwDOIPDwls57fgyU | 15,733 | DOC: AzureChatOpenAI in documentation | {
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] | open | false | null | [] | null | 1 | 2024-01-09T08:21:31 | 2024-01-09T08:23:52 | null | CONTRIBUTOR | null | ### Issue with current documentation:
I just installed langchain 0.1.0 and according to the documentation
https://api.python.langchain.com/en/latest/_modules/langchain_openai/chat_models/azure.html#
AzureChatOpenAI should be in langchain_openai.chat_models but its instead in langchain_community.chat_models
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15732 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15732/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15732/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15732/events | https://github.com/langchain-ai/langchain/issues/15732 | 2,071,773,809 | I_kwDOIPDwls57fMJx | 15,732 | LangchainTracer.on_llm_error callback: IndexError('list index out of range') | {
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"name": "area: memory",
"color": "BFDADC",
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] | open | false | null | [] | null | 3 | 2024-01-09T07:20:53 | 2024-01-10T11:22:58 | null | NONE | null | ### System Info
This is a random occurrence. Maybe after I ask many questions
when it happen, Only clear the memory can recover.
the code to ask:
async for chunk in runnable.astream( #or call astream_log
question,
config
):
await res.stream_token(chunk)
error information:
2024-01-09 13:32:02 - Error in LangchainTracer.on_llm_error callback: IndexError('list index out of range')
2024-01-09 13:32:02 -
Traceback (most recent call last):
File "/usr/local/lib/python3.11/site-packages/chainlit/utils.py", line 39, in wrapper
return await user_function(**params_values)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/rag-app/main.py", line 164, in onMessage
await app.question_anwsering(message.content, False)
File "/rag-app/app.py", line 367, in question_anwsering
async for chunk in runnable.astream_log(
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 752, in astream_log
await task
File "/usr/local/lib/python3.11/asyncio/futures.py", line 290, in __await__
return self.result() # May raise too.
^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/asyncio/futures.py", line 203, in result
raise self._exception.with_traceback(self._exception_tb)
File "/usr/local/lib/python3.11/asyncio/tasks.py", line 267, in __step
result = coro.send(None)
^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 706, in consume_astream
async for chunk in self.astream(input, config, **kwargs):
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 2158, in astream
async for chunk in self.atransform(input_aiter(), config, **kwargs):
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 2141, in atransform
async for chunk in self._atransform_stream_with_config(
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 1308, in _atransform_stream_with_config
chunk: Output = await asyncio.create_task( # type: ignore[call-arg]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/asyncio/futures.py", line 287, in __await__
yield self # This tells Task to wait for completion.
^^^^^^^^^^
File "/usr/local/lib/python3.11/asyncio/tasks.py", line 339, in __wakeup
future.result()
File "/usr/local/lib/python3.11/asyncio/futures.py", line 203, in result
raise self._exception.with_traceback(self._exception_tb)
File "/usr/local/lib/python3.11/asyncio/tasks.py", line 267, in __step
result = coro.send(None)
^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 2111, in _atransform
async for output in final_pipeline:
File "/usr/local/lib/python3.11/site-packages/langchain_core/output_parsers/transform.py", line 60, in atransform
async for chunk in self._atransform_stream_with_config(
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 1283, in _atransform_stream_with_config
final_input: Optional[Input] = await py_anext(input_for_tracing, None)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_core/utils/aiter.py", line 62, in anext_impl
return await __anext__(iterator)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_core/utils/aiter.py", line 97, in tee_peer
item = await iterator.__anext__()
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_core/runnables/base.py", line 806, in atransform
async for output in self.astream(final, config, **kwargs):
File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 307, in astream
raise e
File "/usr/local/lib/python3.11/site-packages/langchain_core/language_models/chat_models.py", line 299, in astream
assert generation is not None
^^^^^^^^^^^^^^^^^^^^^^
AssertionError
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [X] Async
### Reproduction
This is a random occurrence. Maybe after I ask many questions
when it happen, Only clear the memory can recover.
### Expected behavior
fix it | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15731 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15731/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15731/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15731/events | https://github.com/langchain-ai/langchain/issues/15731 | 2,071,722,976 | I_kwDOIPDwls57e_vg | 15,731 | DirectoryLoader use_multithreading inconsistent behavior between true and false (and issue with UnstructuredFileLoader and .json files) | {
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] | open | false | null | [] | null | 1 | 2024-01-09T06:38:32 | 2024-01-09T06:47:20 | null | NONE | null | ### System Info
- Langchain 0.1.0
- PHP 6 (a.k.a. Python 3.11.7)
- Windows 9 (a.k.a. Fedora 39)
<details><summary>requirements.txt</summary>
- aiohttp==3.9.1
- aiosignal==1.3.1
- annotated-types==0.6.0
- anyio==4.2.0
- argon2-cffi==23.1.0
- argon2-cffi-bindings==21.2.0
- arrow==1.3.0
- asgiref==3.7.2
- asttokens==2.4.1
- async-lru==2.0.4
- attrs==23.2.0
- Babel==2.14.0
- backoff==2.2.1
- bcrypt==4.1.2
- beautifulsoup4==4.12.2
- bleach==6.1.0
- build==1.0.3
- cachetools==5.3.2
- certifi==2023.11.17
- cffi==1.16.0
- chardet==5.2.0
- charset-normalizer==3.3.2
- chroma-hnswlib==0.7.3
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- click==8.1.7
- coloredlogs==15.0.1
- comm==0.2.1
- dataclasses-json==0.6.3
- debugpy==1.8.0
- decorator==5.1.1
- defusedxml==0.7.1
- Deprecated==1.2.14
- distro==1.9.0
- docarray==0.40.0
- emoji==2.9.0
- executing==2.0.1
- fastapi==0.108.0
- fastjsonschema==2.19.1
- filelock==3.13.1
- filetype==1.2.0
- flatbuffers==23.5.26
- fqdn==1.5.1
- frozenlist==1.4.1
- fsspec==2023.12.2
- gitdb==4.0.11
- GitPython==3.1.40
- google-auth==2.26.1
- googleapis-common-protos==1.62.0
- greenlet==3.0.3
- grpcio==1.60.0
- h11==0.14.0
- httpcore==1.0.2
- httptools==0.6.1
- httpx==0.26.0
- huggingface-hub==0.20.2
- humanfriendly==10.0
- idna==3.6
- importlib-metadata==6.11.0
- importlib-resources==6.1.1
- ipykernel==6.28.0
- ipython==8.19.0
- isoduration==20.11.0
- jedi==0.19.1
- Jinja2==3.1.2
- joblib==1.3.2
- json5==0.9.14
- jsonpatch==1.33
- jsonpath-python==1.0.6
- jsonpointer==2.4
- jsonschema==4.20.0
- jsonschema-specifications==2023.12.1
- jupyter-events==0.9.0
- jupyter-lsp==2.2.1
- jupyter_client==8.6.0
- jupyter_core==5.7.0
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- jupyterlab_pygments==0.3.0
- jupyterlab_server==2.25.2
- kubernetes==28.1.0
- langchain==0.1.0
- langchain-community==0.0.9
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- langchain-openai==0.0.2
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</details>
### Who can help?
@ey
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Code sample to reproduce where `my-codebase` is a directory with a heterogeneous collection of files (.tsx, .json, .ts, .js, .md)
```
# Document loading: Load codebase from local directory
from langchain_community.document_loaders import DirectoryLoader
project_path = "my-codebase"
loader = DirectoryLoader(project_path, use_multithreading=False)
my_codebase_data = loader.load()
```
This creates the following error:
```
{
"name": "ValueError",
"message": "Detected a JSON file that does not conform to the Unstructured schema. partition_json currently only processes serialized Unstructured output.",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[16], line 8
4 project_path = \"my-codebase\"
6 loader = DirectoryLoader(project_path, use_multithreading=False)
----> 8 my_codebase_data = loader.load()
File ~/Repos/chain-repo/.venv/lib64/python3.11/site-packages/langchain_community/document_loaders/directory.py:157, in DirectoryLoader.load(self)
155 else:
156 for i in items:
--> 157 self.load_file(i, p, docs, pbar)
159 if pbar:
160 pbar.close()
File ~/Repos/chain-repo/.venv/lib64/python3.11/site-packages/langchain_community/document_loaders/directory.py:106, in DirectoryLoader.load_file(self, item, path, docs, pbar)
104 logger.warning(f\"Error loading file {str(item)}: {e}\")
105 else:
--> 106 raise e
107 finally:
108 if pbar:
File ~/Repos/chain-repo/.venv/lib64/python3.11/site-packages/langchain_community/document_loaders/directory.py:100, in DirectoryLoader.load_file(self, item, path, docs, pbar)
98 try:
99 logger.debug(f\"Processing file: {str(item)}\")
--> 100 sub_docs = self.loader_cls(str(item), **self.loader_kwargs).load()
101 docs.extend(sub_docs)
102 except Exception as e:
File ~/Repos/chain-repo/.venv/lib64/python3.11/site-packages/langchain_community/document_loaders/unstructured.py:87, in UnstructuredBaseLoader.load(self)
85 def load(self) -> List[Document]:
86 \"\"\"Load file.\"\"\"
---> 87 elements = self._get_elements()
88 self._post_process_elements(elements)
89 if self.mode == \"elements\":
File ~/Repos/chain-repo/.venv/lib64/python3.11/site-packages/langchain_community/document_loaders/unstructured.py:173, in UnstructuredFileLoader._get_elements(self)
170 def _get_elements(self) -> List:
171 from unstructured.partition.auto import partition
--> 173 return partition(filename=self.file_path, **self.unstructured_kwargs)
File ~/Repos/chain-repo/.venv/lib64/python3.11/site-packages/unstructured/partition/auto.py:480, in partition(filename, content_type, file, file_filename, url, include_page_breaks, strategy, encoding, paragraph_grouper, headers, skip_infer_table_types, ssl_verify, ocr_languages, languages, detect_language_per_element, pdf_infer_table_structure, pdf_extract_images, pdf_extract_element_types, pdf_image_output_dir_path, pdf_extract_to_payload, xml_keep_tags, data_source_metadata, metadata_filename, request_timeout, hi_res_model_name, model_name, **kwargs)
478 elif filetype == FileType.JSON:
479 if not is_json_processable(filename=filename, file=file):
--> 480 raise ValueError(
481 \"Detected a JSON file that does not conform to the Unstructured schema. \"
482 \"partition_json currently only processes serialized Unstructured output.\",
483 )
484 elements = partition_json(filename=filename, file=file, **kwargs)
485 elif (filetype == FileType.XLSX) or (filetype == FileType.XLS):
ValueError: Detected a JSON file that does not conform to the Unstructured schema. partition_json currently only processes serialized Unstructured output."
}
```
### Expected behavior
To get the expected behavior, set `use_multithreading` to True:
```
loader = DirectoryLoader(project_path, use_multithreading=True)
```
Doing this loads the files without error.
Curiously, I get the same loader if I just set `silent_errors` to True:
```
loader = DirectoryLoader(project_path, use_multithreading=False, silent_errors=True)
```
In this case, the error is printed, but the execution is not halted.
Curiously, if I set `use_multithreading` to True and have `silent_errors` set to True, I get the same behaviour as for `use_multithreading=False`. This time it acknowledges that there are errors, where as if it is silent, it just ignores them and doesn't even print them.
```
loader = DirectoryLoader(project_path, use_multithreading=True, silent_errors=True)
```
### Additional thoughts
- This might need to be broken up into different issues
- I am also noticing that the `recursive` parameter is set to False by default, but it still recursively goes through each subdirectory in the directory, is this expected?. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15730 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15730/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15730/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15730/events | https://github.com/langchain-ai/langchain/issues/15730 | 2,071,675,818 | I_kwDOIPDwls57e0Oq | 15,730 | ValueError: cannot reshape array of size 293 into shape (193,121,newaxis) | {
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] | closed | true | null | [] | null | 1 | 2024-01-09T05:52:40 | 2024-01-09T14:40:30 | 2024-01-09T14:40:30 | NONE | null | ### System Info
Ubuntu 20.04
I got this while reading a book pdf with extract_images=True.
[113](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:113) if xObject[obj]["[/Filter](https://file+.vscode-resource.vscode-cdn.net/Filter)"][1:] in _PDF_FILTER_WITHOUT_LOSS:
[114](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:114) height, width = xObject[obj]["[/Height](https://file+.vscode-resource.vscode-cdn.net/Height)"], xObject[obj]["[/Width](https://file+.vscode-resource.vscode-cdn.net/Width)"]
[116](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:116) images.append(
--> [117](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:117) np.frombuffer(xObject[obj].get_data(), dtype=np.uint8).reshape(
[118](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:118) height, width, -1
[119](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:119) )
[120](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:120) )
[121](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:121) elif xObject[obj]["[/Filter](https://file+.vscode-resource.vscode-cdn.net/Filter)"][1:] in _PDF_FILTER_WITH_LOSS:
[122](https://file+.vscode-resource.vscode-cdn.net/home/karan/kj_workspace/kj_argentelm/risk_assessment/backend/~/anaconda3/envs/python39/lib/python3.9/site-packages/langchain_community/document_loaders/parsers/pdf.py:122) images.append(xObject[obj].get_data())
ValueError: cannot reshape array of size 293 into shape (193,121,newaxis)
### Who can help?
@eyurtsev
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("./book.pdf", extract_images=True)
```
### Expected behavior
It should load the pdf and extract info from images also. When I set extract_images=False it works fine. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15729 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15729/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15729/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15729/events | https://github.com/langchain-ai/langchain/issues/15729 | 2,071,616,292 | I_kwDOIPDwls57elsk | 15,729 | Issue: mechanism of embedding parameters in Neo4j Vector object | {
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] | closed | false | null | [] | null | 1 | 2024-01-09T04:39:49 | 2024-01-10T02:55:08 | 2024-01-10T02:55:08 | NONE | null | ### Issue you'd like to raise.
I've seen in the langchain documentation code for vector search in Neo4j which take `OpenAIEmbeddings()` as an object parameter in order to make an embedding for input query
```python
index_name = "vector" # default index name
store = Neo4jVector.from_existing_index(
OpenAIEmbeddings(),
url=url,
username=username,
password=password,
index_name=index_name,
)
```
What i wonder is that, can we pass another embedding model e.g. huggingface model into that parameter instead of openai itself because it many cases, there exist an incompatible dimension when we already have an existing index that is embedded by another off-the-shelf model rather than a embedding model from OpenAI ?
Moreover, i took a look at the source code in case that there has no way to add huggingface model
https://github.com/langchain-ai/langchain/blob/04caf07dee2e2843ab720e5b8f0c0e83d0b86a3e/libs/community/langchain_community/vectorstores/neo4j_vector.py#L111-L147
what i've found is that for the `embedding` parameters of `Neo4jVector` object, it should be Any embedding function implementing`langchain.embeddings.base.Embeddings` interface. Here is the code described that class
https://github.com/langchain-ai/langchain/blob/04caf07dee2e2843ab720e5b8f0c0e83d0b86a3e/libs/core/langchain_core/embeddings.py#L7-L24
Does it mean that we must construct a class that inherits from it in order to implement it effectively, if yes, please provide an example to do it.
### Suggestion:
Any suggested way from this case, if it is currently not supported huggingface model with Neo4j VectorStore, i will help contributing it and make a PR then. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15728 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15728/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15728/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15728/events | https://github.com/langchain-ai/langchain/issues/15728 | 2,071,551,848 | I_kwDOIPDwls57eV9o | 15,728 | Unable to retrieve my prompt when starting the conversation | {
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] | closed | true | null | [] | null | 2 | 2024-01-09T03:13:25 | 2024-01-09T14:39:02 | 2024-01-09T14:39:02 | NONE | null | ### Issue you'd like to raise.
import os
from urllib.parse import quote_plus
from langchain.vectorstores.pgvector import PGVector
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate, MessagesPlaceholder,
HumanMessagePromptTemplate
os.environ['OPENAI_API_KEY'] = "mykey"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host="192.xxx.xx.xxx",
port=5432,
database="xxx",
user="xxx",
password=quote_plus("xx@xxxxxr"),
)
vectordb = PGVector(embedding_function=embeddings,
collection_name="tmp06",
connection_string=CONNECTION_STRING,
)
prompt = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(
"請把使用者的對話紀錄當作參考作為回覆，回答只能使用繁體中文字"
),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
retriever = vectordb.as_retriever()
memory = ConversationTokenBufferMemory(
llm=llm,
prompt=prompt,
memory_key="chat_history",
return_messages=True,
)
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({})
question = input('提問：')
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
vectordb.add_vector(res)
select_vdb = vectordb.nearest(res, n=1)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
### Suggestion:
translates to "Unable to retrieve my prompt when starting the conversation" in English | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15727 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15727/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15727/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15727/events | https://github.com/langchain-ai/langchain/pull/15727 | 2,071,437,491 | PR_kwDOIPDwls5jh1uD | 15,727 | Make packages optional | {
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] | closed | false | null | [] | null | 1 | 2024-01-09T00:35:53 | 2024-01-09T01:09:22 | 2024-01-09T01:09:21 | COLLABORATOR | null | So we don't have to instruct people to modify the Dockerfile every time they delete the packages directory.
See: https://stackoverflow.com/questions/70096208/dockerfile-copy-folder-if-it-exists-conditional-copy/70096420#70096420
Tested on a new repo | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15726 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15726/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15726/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15726/events | https://github.com/langchain-ai/langchain/pull/15726 | 2,071,407,280 | PR_kwDOIPDwls5jhvOO | 15,726 | community: prometheus metrics callback handler | {
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] | null | 3 | 2024-01-08T23:55:54 | 2024-01-16T21:51:33 | null | NONE | null | Description:
Create a new MetricsCallbackHandler to collect token usage and latency metrics for openai. Prometheus metrics are crucial for modern monitoring and enterprise applications because they provide a powerful and flexible way to collect and aggregate real-time metrics about system performance and health. This enables organizations to proactively identify and address issues, ensuring high availability and reliability of their services.
It only collects metrics. It's still up to the application to expose them
Here is an example how to expose metrics in user application. This is not part of this PR which only collects metrics.
```
from flask import Flask, Response, make_response
# Create the Flask App
app = Flask(__name__)
@app.route('/metrics')
def metrics():
return Response(generate_latest(), mimetype=CONTENT_TYPE_LATEST)
```
Dependencies:
prometheus_client-0.19.0 is already specified under libs/community's poetry.
Twitter handle: itestmycode
@baskaryan, @eyurtsev, @hwchase17.
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https://api.github.com/repos/langchain-ai/langchain/issues/15725 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15725/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15725/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15725/events | https://github.com/langchain-ai/langchain/pull/15725 | 2,071,263,800 | PR_kwDOIPDwls5jhPlf | 15,725 | Fixing the SlackGetChannel Tool Input Error | {
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@baskaryan. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15724 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15724/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15724/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15724/events | https://github.com/langchain-ai/langchain/pull/15724 | 2,071,216,847 | PR_kwDOIPDwls5jhFIn | 15,724 | experimental[patch]: minimum version bump | {
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- actually 0.1.5
- actually 0.1.7
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Updated pyproject dependency as well as minimum version test. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15719 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15719/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15719/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15719/events | https://github.com/langchain-ai/langchain/issues/15719 | 2,071,122,353 | I_kwDOIPDwls57ctGx | 15,719 | promt result | {
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i have question, is it possible to get two different type answer from one prompt ? i want to change my question from nlp to queries sql or it will return common answer from chatgpt, for example "show data purchasing" the answer will queries and if the question "show me rate usd today" it will result from the internet
### Suggestion:
_No response_ | {
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```bash
python -m langchain_core.sys_info
``` | {
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- **Issue:** The table In this page : [https://python.langchain.com/docs/integrations/llms/](https://urldefense.com/v3/__https://python.langchain.com/docs/integrations/llms/__;!!ACWV5N9M2RV99hQ!Jqw8gWnQrL1H6blPiGN10jrh1TDAzqGcKAaTAZv7TBy1X_m-03E7T-alOrWY5_71R8QUdONvF2wMRK54D50$) is built based on old module. The proposed fix is to modify the import from langchain-commnuity.
- **Dependencies:** N/A
- **Twitter handle:** N/A
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```➜ ~ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 22.04.3 LTS
Release: 22.04
Codename: jammy
```
```
In [2]: langchain.__version__
Out[2]: '0.0.354'
```
```
In [4]: from langchain_core import __version__
In [5]: __version__
Out[5]: '0.1.8'
```
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
# Description
Create extraction chain pydantic does not work for valid pydantic schemas.
```python
from typing import Optional, List
from langchain.chains import create_extraction_chain_pydantic
from langchain_openai import ChatOpenAI
class Person(BaseModel):
"""Identifying information about a person in a text."""
person_name: str
person_height: Optional[int]
person_hair_color: Optional[str]
dog_breed: Optional[str]
dog_name: Optional[str]
# Chain for an extraction approach based on OpenAI Functions
extraction_chain = create_extraction_chain_pydantic(Person, ChatOpenAI(temperature=0))
extraction_chain.invoke("My name is tom and i'm 6 feet tall")
```
However, more complex pydantic definitions fail:
```
class People(BaseModel):
"""Identifying information about all people in a text."""
__root__: List[Person]
# Chain for an extraction approach based on OpenAI Functions
extraction_chain = create_extraction_chain_pydantic(People, ChatOpenAI(temperature=0))
extraction_chain.invoke("My name is tom and i'm 6 feet tall")
```
```
class NestedPeople(BaseModel):
"""Identifying information about all people in a text."""
people: List[Person]
# Chain for an extraction approach based on OpenAI Functions
extraction_chain = create_extraction_chain_pydantic(NestedPeople, ChatOpenAI(temperature=0))
extraction_chain.invoke("My name is tom and i'm 6 feet tall")
```
---
## Acceptance criteria
1. Code does not affect backwards compatibility if possible If must be a breaking change, perhaps we should create a new function for this purpose.
2. Should we replace LLMChain with an LCEL chain and determine what is the correct output interface for extractions? User may want error information to be returned rather than raised.
3. Unit-tests must cover above cases
### Expected behavior
All shown cases should work properly and not fail during initialization time. | {
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Fix for: https://github.com/langchain-ai/langserve/issues/301
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https://api.github.com/repos/langchain-ai/langchain/issues/15709 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15709/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15709/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15709/events | https://github.com/langchain-ai/langchain/pull/15709 | 2,070,667,943 | PR_kwDOIPDwls5jfM2M | 15,709 | core(minor): Add bulk add messages to BaseChatMessageHistory interface | {
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] | open | false | null | [] | null | 3 | 2024-01-08T15:17:48 | 2024-01-17T13:50:41 | null | COLLABORATOR | null | * Add bulk add_messages method to the interface.
* Update documentation for add_ai_message and add_human_message to denote them as being marked for deprecation. We should stop using them as they create more incorrect (inefficient) ways of doing things
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https://api.github.com/repos/langchain-ai/langchain/issues/15707 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15707/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15707/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15707/events | https://github.com/langchain-ai/langchain/pull/15707 | 2,070,596,274 | PR_kwDOIPDwls5je9TT | 15,707 | langchain: Extract _aperform_agent_action from _aiter_next_step from AgentExecutor | {
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] | open | false | null | [] | null | 4 | 2024-01-08T14:39:37 | 2024-01-15T11:47:00 | null | NONE | null | <!-- Thank you for contributing to LangChain!
Please title your PR "lang: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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- **Description:** extreact the _aperform_agent_action in the AgentExecutor class to allow for easier overriding. Extracted logic from _iter_next_step into a new method _perform_agent_action for consistency and easier overriding.
- **Issue:** #15706
Closes #15706 | {
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] | open | false | null | [] | null | 1 | 2024-01-08T14:12:40 | 2024-01-08T14:35:19 | null | NONE | null | ### Feature request
I'm trying to extend AgentExecutor with custom logic and I want to override how the agent perform actions.
What i'd really need is only to override the __aperform_agent_action_ function; however this function is defined in the __aiter_next_step_ function, making it necessary to override the whole function.
This obviously comes with the drawbacks of more code and having to reconciliate future updates.
In my opinion, the __aiter_next_step_ function could be extracted into an instance or static method, allowing to override only the relevant parts.
Also, for the synchronous version _iter_next_step_ a similar problem arises, as the __perform_agent_action_ is not defined at all.
The relevant code can be extracted in a method making it easier to override it.
### Motivation
This update would allow for better extendibility of the AgentExecutor class
### Your contribution
I can submit a PR to address the issue | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15705 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15705/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15705/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15705/events | https://github.com/langchain-ai/langchain/issues/15705 | 2,070,541,205 | I_kwDOIPDwls57afOV | 15,705 | DOC: Data Pipeline for humans | {
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Dear all
I have this pipeline
```python
translation_cache = ToJSON(key=key, out_dir=Path("results/sabadel/translation"))
translation_prompt = Prompt.from_yaml(Path("prompts/translate.yml"))
translation_chain = (
{
"transcription": lambda data: format_transcription_for_prompt(
data["transcription"]
)
}
| translation_prompt.template
| model
| {
"transcription": RunnableLambda(
lambda res: translation_output_parser(res, transcription)
)
}
| {"transcription": lambda x: translation_cache(x)}
)
translation_chain = (
RunnableLambda(lambda data: {**data, "transcription": translation_cache.load()})
if translation_cache.exists
else translation_chain
)
# evaluation
evaluation_prompt = Prompt.from_yaml(Path("prompts/sabadell/evaluation.yml"))
evaluation_cache = ToJSON(key=key, out_dir=Path("results/sabadel/evaluation"))
evaluation_chain = (
evaluation_prompt.template | model | evaluation_output_parser | evaluation_cache
)
evaluation_chain = (
RunnableLambda(lambda data: {**data, "evaluations": evaluation_cache.load()})
if evaluation_cache.exists
else evaluation_chain
)
# retention
retention_prompt = Prompt.from_yaml(Path("prompts/sabadell/evaluation.retention.yml"))
retention_cache = ToJSON(key=key, out_dir=Path("results/sabadel/retention"))
retention_chain = (
retention_prompt.template | model | retention_output_parser | retention_cache
)
retention_chain = RunnableLambda(
lambda data: {
**data,
"retention": retention_cache.load()
if retention_cache.exists
else retention_chain(**data),
}
)
# final chain
# chain = translation_chain | retention_chain
# print(translation_chain.invoke({"transcription": transcription}))
chain = translation_chain | evaluation_chain | retention_chain
print(chain.get_graph().print_ascii())
print(
chain.invoke({"transcription": transcription, "retention_script": retention_script})
)
```
Now what I'd like to do is that `evaluation_chain` puts stuff into a key `evaluations` and pass along the original data dict + that key to `retention_chain` and `retention_chain` should put it's output into a `retention` data key and then pass along the original dict + all the outputs
how to do it?
### Idea or request for content:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15703 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15703/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15703/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15703/events | https://github.com/langchain-ai/langchain/pull/15703 | 2,070,474,093 | PR_kwDOIPDwls5jeihs | 15,703 | Add doc for AstraDB document loader | {
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] | closed | false | null | [] | null | 1 | 2024-01-08T13:34:39 | 2024-01-08T20:54:14 | 2024-01-08T20:21:46 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
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https://api.github.com/repos/langchain-ai/langchain/issues/15702 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15702/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15702/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15702/events | https://github.com/langchain-ai/langchain/issues/15702 | 2,070,370,106 | I_kwDOIPDwls57Z1c6 | 15,702 | Add support for the Mistral AI Embedding Model | {
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] | open | false | null | [] | null | 2 | 2024-01-08T12:35:54 | 2024-01-09T00:24:57 | null | NONE | null | ### Feature request
Feature request
They provide a [python client](https://docs.mistral.ai/platform/endpoints/) to access the embedding model
### Motivation
It would be great if we added the new embedding service from Mistral!
### Your contribution
I can work on this and submit a PR | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15700 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15700/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15700/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15700/events | https://github.com/langchain-ai/langchain/issues/15700 | 2,070,146,142 | I_kwDOIPDwls57Y-xe | 15,700 | DOC: Quickstart Code Fails for Retrieval Chain | {
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] | closed | false | null | [] | null | 5 | 2024-01-08T10:23:26 | 2024-01-08T15:54:43 | 2024-01-08T15:54:43 | NONE | null | ### Issue with current documentation:
Hi.
I am a newcomer to Langchain following the Quickstart tutorial in a Jupyter Notebook, using the setup recommended by the installation guide. I am following the OpenAI tutorial, rather than the local LLM version.
I followed the exact code in the docs by pasting the cells into my notebook. All code works perfectly without a single error or warning. However, the code fails at this point:
```python
response = retrieval_chain.invoke({"input": "how can langsmith help with testing?"})
print(response["answer"])
# LangSmith offers several features that can help with testing:...
```
When I attempt to run this code, I get the following output in my notebook:
```
{
"name": "ValidationError",
"message": "2 validation errors for DocArrayDoc
text
Field required [type=missing, input_value={'embedding': [-0.0144587... -0.015377209573652503]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing
metadata
Field required [type=missing, input_value={'embedding': [-0.0144587... -0.015377209573652503]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing",
"stack": "---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[18], line 1
----> 1 response = retrieval_chain.invoke({\"input\": \"how can langsmith help with testing?\"})
2 print(response[\"answer\"])
4 # LangSmith offers several features that can help with testing:...
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3590, in RunnableBindingBase.invoke(self, input, config, **kwargs)
3584 def invoke(
3585 self,
3586 input: Input,
3587 config: Optional[RunnableConfig] = None,
3588 **kwargs: Optional[Any],
3589 ) -> Output:
-> 3590 return self.bound.invoke(
3591 input,
3592 self._merge_configs(config),
3593 **{**self.kwargs, **kwargs},
3594 )
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:1762, in RunnableSequence.invoke(self, input, config)
1760 try:
1761 for i, step in enumerate(self.steps):
-> 1762 input = step.invoke(
1763 input,
1764 # mark each step as a child run
1765 patch_config(
1766 config, callbacks=run_manager.get_child(f\"seq:step:{i+1}\")
1767 ),
1768 )
1769 # finish the root run
1770 except BaseException as e:
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\passthrough.py:415, in RunnableAssign.invoke(self, input, config, **kwargs)
409 def invoke(
410 self,
411 input: Dict[str, Any],
412 config: Optional[RunnableConfig] = None,
413 **kwargs: Any,
414 ) -> Dict[str, Any]:
--> 415 return self._call_with_config(self._invoke, input, config, **kwargs)
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:975, in Runnable._call_with_config(self, func, input, config, run_type, **kwargs)
971 context = copy_context()
972 context.run(var_child_runnable_config.set, child_config)
973 output = cast(
974 Output,
--> 975 context.run(
976 call_func_with_variable_args,
977 func, # type: ignore[arg-type]
978 input, # type: ignore[arg-type]
979 config,
980 run_manager,
981 **kwargs,
982 ),
983 )
984 except BaseException as e:
985 run_manager.on_chain_error(e)
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\config.py:323, in call_func_with_variable_args(func, input, config, run_manager, **kwargs)
321 if run_manager is not None and accepts_run_manager(func):
322 kwargs[\"run_manager\"] = run_manager
--> 323 return func(input, **kwargs)
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\passthrough.py:402, in RunnableAssign._invoke(self, input, run_manager, config, **kwargs)
389 def _invoke(
390 self,
391 input: Dict[str, Any],
(...)
394 **kwargs: Any,
395 ) -> Dict[str, Any]:
396 assert isinstance(
397 input, dict
398 ), \"The input to RunnablePassthrough.assign() must be a dict.\"
400 return {
401 **input,
--> 402 **self.mapper.invoke(
403 input,
404 patch_config(config, callbacks=run_manager.get_child()),
405 **kwargs,
406 ),
407 }
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:2327, in RunnableParallel.invoke(self, input, config)
2314 with get_executor_for_config(config) as executor:
2315 futures = [
2316 executor.submit(
2317 step.invoke,
(...)
2325 for key, step in steps.items()
2326 ]
-> 2327 output = {key: future.result() for key, future in zip(steps, futures)}
2328 # finish the root run
2329 except BaseException as e:
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:2327, in <dictcomp>(.0)
2314 with get_executor_for_config(config) as executor:
2315 futures = [
2316 executor.submit(
2317 step.invoke,
(...)
2325 for key, step in steps.items()
2326 ]
-> 2327 output = {key: future.result() for key, future in zip(steps, futures)}
2328 # finish the root run
2329 except BaseException as e:
File C:\\ProgramData\\miniconda3\\Lib\\concurrent\\futures\\_base.py:456, in Future.result(self, timeout)
454 raise CancelledError()
455 elif self._state == FINISHED:
--> 456 return self.__get_result()
457 else:
458 raise TimeoutError()
File C:\\ProgramData\\miniconda3\\Lib\\concurrent\\futures\\_base.py:401, in Future.__get_result(self)
399 if self._exception:
400 try:
--> 401 raise self._exception
402 finally:
403 # Break a reference cycle with the exception in self._exception
404 self = None
File C:\\ProgramData\\miniconda3\\Lib\\concurrent\\futures\\thread.py:58, in _WorkItem.run(self)
55 return
57 try:
---> 58 result = self.fn(*self.args, **self.kwargs)
59 except BaseException as exc:
60 self.future.set_exception(exc)
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:3590, in RunnableBindingBase.invoke(self, input, config, **kwargs)
3584 def invoke(
3585 self,
3586 input: Input,
3587 config: Optional[RunnableConfig] = None,
3588 **kwargs: Optional[Any],
3589 ) -> Output:
-> 3590 return self.bound.invoke(
3591 input,
3592 self._merge_configs(config),
3593 **{**self.kwargs, **kwargs},
3594 )
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\runnables\\base.py:1762, in RunnableSequence.invoke(self, input, config)
1760 try:
1761 for i, step in enumerate(self.steps):
-> 1762 input = step.invoke(
1763 input,
1764 # mark each step as a child run
1765 patch_config(
1766 config, callbacks=run_manager.get_child(f\"seq:step:{i+1}\")
1767 ),
1768 )
1769 # finish the root run
1770 except BaseException as e:
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\retrievers.py:121, in BaseRetriever.invoke(self, input, config)
117 def invoke(
118 self, input: str, config: Optional[RunnableConfig] = None
119 ) -> List[Document]:
120 config = ensure_config(config)
--> 121 return self.get_relevant_documents(
122 input,
123 callbacks=config.get(\"callbacks\"),
124 tags=config.get(\"tags\"),
125 metadata=config.get(\"metadata\"),
126 run_name=config.get(\"run_name\"),
127 )
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\retrievers.py:223, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)
221 except Exception as e:
222 run_manager.on_retriever_error(e)
--> 223 raise e
224 else:
225 run_manager.on_retriever_end(
226 result,
227 **kwargs,
228 )
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\retrievers.py:216, in BaseRetriever.get_relevant_documents(self, query, callbacks, tags, metadata, run_name, **kwargs)
214 _kwargs = kwargs if self._expects_other_args else {}
215 if self._new_arg_supported:
--> 216 result = self._get_relevant_documents(
217 query, run_manager=run_manager, **_kwargs
218 )
219 else:
220 result = self._get_relevant_documents(query, **_kwargs)
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_core\\vectorstores.py:654, in VectorStoreRetriever._get_relevant_documents(self, query, run_manager)
650 def _get_relevant_documents(
651 self, query: str, *, run_manager: CallbackManagerForRetrieverRun
652 ) -> List[Document]:
653 if self.search_type == \"similarity\":
--> 654 docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
655 elif self.search_type == \"similarity_score_threshold\":
656 docs_and_similarities = (
657 self.vectorstore.similarity_search_with_relevance_scores(
658 query, **self.search_kwargs
659 )
660 )
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_community\\vectorstores\\docarray\\base.py:127, in DocArrayIndex.similarity_search(self, query, k, **kwargs)
115 def similarity_search(
116 self, query: str, k: int = 4, **kwargs: Any
117 ) -> List[Document]:
118 \"\"\"Return docs most similar to query.
119
120 Args:
(...)
125 List of Documents most similar to the query.
126 \"\"\"
--> 127 results = self.similarity_search_with_score(query, k=k, **kwargs)
128 return [doc for doc, _ in results]
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\langchain_community\\vectorstores\\docarray\\base.py:106, in DocArrayIndex.similarity_search_with_score(self, query, k, **kwargs)
94 \"\"\"Return docs most similar to query.
95
96 Args:
(...)
103 Lower score represents more similarity.
104 \"\"\"
105 query_embedding = self.embedding.embed_query(query)
--> 106 query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
107 docs, scores = self.doc_index.find(query_doc, search_field=\"embedding\", limit=k)
109 result = [
110 (Document(page_content=doc.text, metadata=doc.metadata), score)
111 for doc, score in zip(docs, scores)
112 ]
File e:\\Repos\\Practice Projects\\Langchain\\Quickstart\\quickstart\\Lib\\site-packages\\pydantic\\main.py:164, in BaseModel.__init__(__pydantic_self__, **data)
162 # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks
163 __tracebackhide__ = True
--> 164 __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
ValidationError: 2 validation errors for DocArrayDoc
text
Field required [type=missing, input_value={'embedding': [-0.0144587... -0.015377209573652503]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing
metadata
Field required [type=missing, input_value={'embedding': [-0.0144587... -0.015377209573652503]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.5/v/missing"
}
```
### Idea or request for content:
As I am a newcomer, I do not understand exactly what the issue is. Thus, I would like to request that the documentation be updated so that the code works correctly. In the meantime, I would appreciate any assistance so I can continue to learn Langchain through the quickstart and work my way through the rest of the docs . | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15699 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15699/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15699/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15699/events | https://github.com/langchain-ai/langchain/pull/15699 | 2,070,130,611 | PR_kwDOIPDwls5jdW5w | 15,699 | Fix AstraDB logical operator filtering | {
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] | closed | false | null | [] | null | 2 | 2024-01-08T10:16:35 | 2024-01-08T20:53:31 | 2024-01-08T20:23:46 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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,
- **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` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
This change fixes the AstraDB logical operator filtering (`$and,` `$or`).
The `metadata` prefix must not be added if the key is `$and` or `$or`.
| {
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https://api.github.com/repos/langchain-ai/langchain/issues/15698 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15698/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15698/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15698/events | https://github.com/langchain-ai/langchain/issues/15698 | 2,070,099,650 | I_kwDOIPDwls57YzbC | 15,698 | TypeError: SlackGetChannel._run() got multiple values for argument 'run_manager' | {
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] | open | false | null | [] | null | 2 | 2024-01-08T09:58:38 | 2024-01-08T10:10:03 | null | NONE | null | ### System Info
Python 3.10.12
langchain 0.0.354
### Who can help?
@hwch
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents.agent_toolkits.slack.toolkit import SlackToolkit
stoolkit = SlackToolkit()
tools = stoolkit.get_tools()
agent = OpenAIAssistantRunnable.create_assistant(
name="Sales assistant",
instructions="""You are a admin agent, tasked with the following jobs:
2. Read and post messages on Slack""",
tools=tools,
model="gpt-4-1106-preview",
as_agent=True
)
from langchain.agents.agent import AgentExecutor
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
agent_executor.invoke({"content":"list all messages in #budget-decisions"})
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[11], line 13
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
---> agent_executor.invoke({"content":"list all messages in #budget-decisions"})
File ~/smith/lib/python3.10/site-packages/langchain/chains/base.py:93, in Chain.invoke(self, input, config, **kwargs)
86 def invoke(
87 self,
88 input: Dict[str, Any],
89 config: Optional[RunnableConfig] = None,
90 **kwargs: Any,
91 ) -> Dict[str, Any]:
92 config = ensure_config(config)
---> 93 return self(
94 input,
95 callbacks=config.get("callbacks"),
96 tags=config.get("tags"),
97 metadata=config.get("metadata"),
98 run_name=config.get("run_name"),
99 **kwargs,
100 )
File ~/smith/lib/python3.10/site-packages/langchain/chains/base.py:316, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
314 except BaseException as e:
315 run_manager.on_chain_error(e)
--> 316 raise e
317 run_manager.on_chain_end(outputs)
318 final_outputs: Dict[str, Any] = self.prep_outputs(
319 inputs, outputs, return_only_outputs
320 )
File ~/smith/lib/python3.10/site-packages/langchain/chains/base.py:310, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
303 run_manager = callback_manager.on_chain_start(
304 dumpd(self),
305 inputs,
306 name=run_name,
307 )
308 try:
309 outputs = (
--> 310 self._call(inputs, run_manager=run_manager)
311 if new_arg_supported
312 else self._call(inputs)
313 )
314 except BaseException as e:
315 run_manager.on_chain_error(e)
File ~/smith/lib/python3.10/site-packages/langchain/agents/agent.py:1312, in AgentExecutor._call(self, inputs, run_manager)
1310 # We now enter the agent loop (until it returns something).
1311 while self._should_continue(iterations, time_elapsed):
-> 1312 next_step_output = self._take_next_step(
1313 name_to_tool_map,
1314 color_mapping,
1315 inputs,
1316 intermediate_steps,
1317 run_manager=run_manager,
1318 )
1319 if isinstance(next_step_output, AgentFinish):
1320 return self._return(
1321 next_step_output, intermediate_steps, run_manager=run_manager
1322 )
File ~/smith/lib/python3.10/site-packages/langchain/agents/agent.py:1038, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
1029 def _take_next_step(
1030 self,
1031 name_to_tool_map: Dict[str, BaseTool],
(...)
1035 run_manager: Optional[CallbackManagerForChainRun] = None,
1036 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
1037 return self._consume_next_step(
-> 1038 [
1039 a
1040 for a in self._iter_next_step(
1041 name_to_tool_map,
1042 color_mapping,
1043 inputs,
1044 intermediate_steps,
1045 run_manager,
1046 )
1047 ]
1048 )
File ~/smith/lib/python3.10/site-packages/langchain/agents/agent.py:1038, in <listcomp>(.0)
1029 def _take_next_step(
1030 self,
1031 name_to_tool_map: Dict[str, BaseTool],
(...)
1035 run_manager: Optional[CallbackManagerForChainRun] = None,
1036 ) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
1037 return self._consume_next_step(
-> 1038 [
1039 a
1040 for a in self._iter_next_step(
1041 name_to_tool_map,
1042 color_mapping,
1043 inputs,
1044 intermediate_steps,
1045 run_manager,
1046 )
1047 ]
1048 )
File ~/smith/lib/python3.10/site-packages/langchain/agents/agent.py:1134, in AgentExecutor._iter_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
1132 tool_run_kwargs["llm_prefix"] = ""
1133 # We then call the tool on the tool input to get an observation
-> 1134 observation = tool.run(
1135 agent_action.tool_input,
1136 verbose=self.verbose,
1137 color=color,
1138 callbacks=run_manager.get_child() if run_manager else None,
1139 **tool_run_kwargs,
1140 )
1141 else:
1142 tool_run_kwargs = self.agent.tool_run_logging_kwargs()
File ~/smith/lib/python3.10/site-packages/langchain_core/tools.py:365, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)
363 except (Exception, KeyboardInterrupt) as e:
364 run_manager.on_tool_error(e)
--> 365 raise e
366 else:
367 run_manager.on_tool_end(
368 str(observation), color=color, name=self.name, **kwargs
369 )
File ~/smith/lib/python3.10/site-packages/langchain_core/tools.py:337, in BaseTool.run(self, tool_input, verbose, start_color, color, callbacks, tags, metadata, run_name, **kwargs)
334 try:
335 tool_args, tool_kwargs = self._to_args_and_kwargs(parsed_input)
336 observation = (
--> 337 self._run(*tool_args, run_manager=run_manager, **tool_kwargs)
338 if new_arg_supported
339 else self._run(*tool_args, **tool_kwargs)
340 )
341 except ToolException as e:
342 if not self.handle_tool_error:
TypeError: SlackGetChannel._run() got multiple values for argument 'run_manager'
### Expected behavior
The slack agent should send a message on the said channel. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15697 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15697/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15697/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15697/events | https://github.com/langchain-ai/langchain/pull/15697 | 2,070,098,811 | PR_kwDOIPDwls5jdP6L | 15,697 | langchain-docs: Correct the word "iteratively" in use-cases documentation | {
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] | closed | false | null | [] | null | 1 | 2024-01-08T09:58:07 | 2024-01-08T20:24:00 | 2024-01-08T20:24:00 | CONTRIBUTOR | null |
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,
- **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!
**Description:** Fixes the word "iteratively" in the use-cases documentation
**Twitter handle:** @untilhamza | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15696 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15696/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15696/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15696/events | https://github.com/langchain-ai/langchain/issues/15696 | 2,070,080,675 | I_kwDOIPDwls57Yuyj | 15,696 | SelfQueryRetriever, ValueError: Expected where operand value to be a str, int, float, or list of those type | {
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] | open | false | null | [] | null | 10 | 2024-01-08T09:48:39 | 2024-01-08T12:38:23 | null | NONE | null | ### System Info
Chroma 0.4.22
Langchain 0.0.354
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a SelfQueryRetriever
2. Create AttributeInfo metadata list in preparation for filtering based off metadata.
```python
self_query_retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
"Information about when document was published and where it originated from",
metadata_field_info
)
# retriever = MergerRetriever(retrievers=[parent_retriever, self_query_retriever])
retriever = self_query_retriever
template = """
### Instruction: You're an assistant who knows the following information:
### {context}
If you don't know the answer, then say you don't know and refer the user to the respective department for extra information.
Absolutely do not mention you are an AI language model. Use only the chat history and the following information.
### {chat_history}
### Input: {question}
### Response:
""".strip()
prompt = PromptTemplate(input_variables=["context", "chat_history", "question"], template=template)
chain = ConversationalRetrievalChain.from_llm(
llm,
chain_type="stuff",
retriever=retriever,
combine_docs_chain_kwargs={"prompt": prompt},#, "metadata_weights": metadata_weights},
return_source_documents=True,
verbose=False,
rephrase_question=True,
max_tokens_limit=16000,
response_if_no_docs_found="""I'm sorry, but I was not able to find the answer to your question based on the information I know. You may have to reach out to the respective internal department for more details regarding your inquiry."""
)
return chain
def score_unstructured(model, data, query, **kwargs) -> str:
"""Custom model hook for making completions with our knowledge base.
When requesting predictions from the deployment, pass a dictionary
with the following keys:
- 'question' the question to be passed to the retrieval chain
- 'chat_history' (optional) a list of two-element lists corresponding to
preceding dialogue between the Human and AI, respectively
datarobot-user-models (DRUM) handles loading the model and calling
this function with the appropriate parameters.
Returns:
--------
rv : str
Json dictionary with keys:
- 'question' user's original question
- 'chat_history' chat history that was provided with the original question
- 'answer' the generated answer to the question
- 'references' list of references that were used to generate the answer
- 'error' - error message if exception in handling request
"""
import json
try:
chain = model
data_dict = json.loads(data)
if 'chat_history' in data_dict:
chat_history = [(human, ai,) for human, ai in data_dict['chat_history']]
else:
chat_history = []# model.chat_history
rv = chain(
inputs={
'question': data_dict['question'],
'chat_history': chat_history,
},
)
source_docs = rv.pop('source_documents')
rv['references'] = [doc.metadata['source'] for doc in source_docs]
if len(source_docs) > 0:
rv["top_reference_text"] = [doc.page_content for doc in source_docs]
else:
rv["top_reference_text"] = ""
except Exception as e:
rv = {'error': f"{e.__class__.__name__}: {str(e)}"}
return json.dumps(rv)
model = load_model(".")
```
I asked the following question:
```python
questions = ["What is the minimum opening deposit for each account as of January 2023?"]
os.environ["TOKENIZERS_PARALLELISM"] = "false"
for question in questions:
rv = score_unstructured(model, json.dumps(
{
"question": question
# "chat_history": []
}
),
None)
print(rv)
print(question.upper())
print(json.loads(rv)["answer"])
print(json.loads(rv))
print("------------------------------------------------")
```
The issue I got was ```ValueError: Expected where operand value to be a str, int, float, or list of those type, got {'date': '2023-01-01', 'type': 'date'}```
It looks like the SelfQueryRetriever converted my question that had January 2023 to a date object. This date object throws an error. I'm not sure how to resolve this issue on my end.
### Expected behavior
Query with a date and receive an answer from the SelfQueryRetriever. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15695 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15695/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15695/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15695/events | https://github.com/langchain-ai/langchain/issues/15695 | 2,070,079,179 | I_kwDOIPDwls57YubL | 15,695 | Reuse KV-Cache with local LLM (IlamaCpp) instead of expensive reprocessing of all history tokens | {
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] | open | false | null | [] | null | 1 | 2024-01-08T09:47:45 | 2024-01-08T09:50:09 | null | NONE | null | ### Feature request
- I want the local LLM (IlamaCpp) to maintain its context, which will significantly improve the efficiency of follow-up questions.
- Currently, the context of IlamaCpp is lost after the first call, necessitating the reprocessing of all tokens for any subsequent question.
- **Proposed Solution:** Utilize the internal KV cache of IlamaCpp to retain context and avoid reprocessing the same tokens repeatedly.
### Motivation
- My motivation is to address the inefficiency in the current process where the context is not preserved between queries.
- There seems to be no existing solution for this specific issue as per my research, for example, [LangChain Caching Documentation](https://python.langchain.com/docs/modules/model_io/chat/chat_model_caching#in-memory-cache).
minimized example which shows my current workaround:
```
from langchain.llms import LlamaCpp
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from pathlib import Path
class LlmRunner:
def __init__(self, path_to_model: Path) -> None:
self.llm_instance = LlamaCpp(
model_path=str(path_to_model),
n_ctx=16384,
max_tokens=-1,
temperature=0,
repeat_penalty=1.15,
n_gpu_layers=1,
n_threads=8,
verbose=False,
)
def run(self, invoice_text: str):
# Initial processing filling up kv-cache and context
initial_prompt = """
Extract and format keys from the invoice text into JSON.
<context>
{input}
</context>
"""
chain1 = LLMChain(
llm=self.llm_instance,
prompt=ChatPromptTemplate.from_messages([
HumanMessagePromptTemplate.from_template(initial_prompt)
])
)
response1 = chain1.invoke({'input': invoice_text})
# Follow-up processing which COULD reuse the context, but doesn't
follow_up_prompt = """
Review your results and normalize the dates to YYYY-MM-DD.
<input>
{input}
</input>
<context>
{invoice_text}
</context>"""
chain2 = LLMChain(
llm=self.llm_instance,
prompt=ChatPromptTemplate.from_messages([
HumanMessagePromptTemplate.from_template(follow_up_prompt)
])
)
response2 = chain2.invoke({'invoice_text' : invoice_text, 'input': response1['text']})
return response2['text']
# Example usage
path_to_model = Path("path_to_your_model")
runner = LlmRunner(path_to_model)
invoice_text = "Your invoice text here"
result = runner.run(invoice_text)
print(result)
```
### Your contribution
- if something like this already exists I am willing to provide example and update documentation
- if you point me in the right directions and it's just a few 100s LOC I am willing to submit a PR | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15694 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15694/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15694/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15694/events | https://github.com/langchain-ai/langchain/issues/15694 | 2,070,024,246 | I_kwDOIPDwls57YhA2 | 15,694 | feat: add a flag that determines whether to load the milvus collection | {
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] | closed | false | null | [] | null | 1 | 2024-01-08T09:14:35 | 2024-01-15T19:25:25 | 2024-01-15T19:25:25 | CONTRIBUTOR | null | ### Feature request
Every time i create a milvus object, i load the collection, but there is no way to dynamically know the replica_number of the currently loaded collection, so there is a disadvantage that i have to hand over the different replica_number for each collection as an argument. Therefore, when creating a milvus object, I would like to add a flag that can determine whether to load or not.
### Motivation
Always loading a collection can cause an unexpected error.
### Your contribution
https://github.com/langchain-ai/langchain/pull/15693 | {
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] | closed | false | null | [] | null | 3 | 2024-01-08T09:11:10 | 2024-01-15T19:25:24 | 2024-01-15T19:25:24 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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,
- **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` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
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fix https://github.com/langchain-ai/langchain/issues/15694 | {
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<pre>
```
def generate_custom_prompt(query=None, name=None, not_uuid=None, chroma_db_path=None):
check = query.lower()
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
retriever = vectordb.as_retriever(search_kwargs={"k": 2})
relevant_document = retriever.get_relevant_documents(query)
print(relevant_document, "*****************************************")
context_text = "\n\n---\n\n".join([doc.page_content for doc in relevant_document])
user_experience_inst = UserExperience.objects.get(not_uuid=not_uuid)
greetings = ['hi', 'hello', 'hey', 'hui', 'hiiii', 'hii', 'hiii', 'heyyy']
if check in greetings:
custom_prompt_template = f"""
Just simply reply with "Hello {name}! How can I assist you today?"
"""
elif check not in greetings and user_experience_inst.custom_prompt:
custom_prompt_template = f"""Answer the question based only on following context: ```{context_text} ```
You are a chatbot designed to provide answers to User's Questions:```{check}```, delimited by triple backticks.
Generate your answer to match the user's requirements: {user_experience_inst.custom_prompt}
If you encounter a question for which you don't know the answer, please respond with 'I don't know' and refrain from making up an answer.
- Before saying 'I don't know,' please re-verify your vector store to ensure the answer is not present in the database.
Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, feel free to ask for clarification.
User's Question: ```{check}```
AI Answer:"""
else:
custom_prompt_template = f"""Generate your response exclusively from the provided context: {{context_text}}. You function as a chatbot specializing in delivering detailed answers to the User's Question: ```{{check}} ```, enclosed within triple backticks.
Generate your answer in points in the following format:
1. Point no 1
1.1 Its subpoint in details
1.2 More information if needed.
2. Point no 2
2.1 Its subpoint in details
2.2 More information if needed.
…
N. Another main point.
If you encounter a question for which you don't know the answer based on the predefined points, please respond with 'I don't know' and refrain from making up an answer.
However, if the answer is not present in the predefined points, then Provide comprehensive information related to the user's query.
Remember, your goal is to assist the user in the best way possible. If the question is unclear or ambiguous, you can ask for clarification.
User's Question: ```{{check}} ```
AI Answer:"""
custom_prompt = ChatPromptTemplate.from_template(template=custom_prompt_template)
formatted_prompt = custom_prompt.format(context_text=context_text, check=check)
llm = ChatOpenAI(temperature=0.1)
memory = ConversationBufferMemory(llm=llm, output_key='answer', memory_key='chat_history', return_messages=True)
qa = ConversationalRetrievalChain.from_llm(llm=llm, memory=memory, chain_type="stuff", retriever=retriever,
return_source_documents=True, get_chat_history=lambda h: h, verbose=True,
combine_docs_chain_kwargs={"prompt": PromptTemplate(
template=custom_prompt_template, input_variables=["context_text", "check"])})
return qa
```
</pre>
How to add chat history in prompt template as well in above function
### Suggestion:
_No response_ | {
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
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] | open | false | null | [] | null | 1 | 2024-01-08T08:06:51 | 2024-01-08T08:16:23 | null | CONTRIBUTOR | null | ### Feature request
I suggest supporting the Milvus vector database's new [Dynamic Schema](https://milvus.io/docs/dynamic_schema.md) feature.
### Motivation
According to Milvus:
> Dynamic schema enables users to insert entities with new fields into a Milvus collection without modifying the existing schema. This means that users can insert data without knowing the full schema of a collection and can include fields that are not yet defined.
I think it is good to allow Langchain to have this feature when multiple types or schema of documents are added to the database.
### Your contribution
I propose to add a "dynamic_schema" flag to the `__init__` and `from_texts` method of the Milvus class:
`__init__` method:
https://github.com/langchain-ai/langchain/blob/4c47f39fcb539fdeff6dd6d9b1f483cd9a1af69b/libs/community/langchain_community/vectorstores/milvus.py#L107-L125
Change to:
```python
def __init__(
self,
embedding_function: Embeddings,
collection_name: str = "LangChainCollection",
collection_description: str = "",
connection_args: Optional[dict[str, Any]] = None,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: Optional[bool] = False,
*,
primary_field: str = "pk",
text_field: str = "text",
vector_field: str = "vector",
metadata_field: Optional[str] = None,
partition_names: Optional[list] = None,
replica_number: int = 1,
timeout: Optional[float] = None,
dynamic_schema = False,
):
```
`from_texts` method:
https://github.com/langchain-ai/langchain/blob/4c47f39fcb539fdeff6dd6d9b1f483cd9a1af69b/libs/community/langchain_community/vectorstores/milvus.py#L839-L887
Change to:
```python
def from_texts(
cls,
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
collection_name: str = "LangChainCollection",
connection_args: dict[str, Any] = DEFAULT_MILVUS_CONNECTION,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: bool = False,
dynamic_schema = False,
**kwargs: Any,
) -> Milvus:
```
I may later submit a PR for this suggestion. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15689 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15689/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15689/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15689/events | https://github.com/langchain-ai/langchain/issues/15689 | 2,069,914,553 | I_kwDOIPDwls57YGO5 | 15,689 | Issue: <How can I extract vector data from pgvector for use as a reference in the next conversation to enable long-term memory functionality for my chatbot?> | {
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] | open | false | null | [] | null | 1 | 2024-01-08T07:57:56 | 2024-01-08T08:06:51 | null | NONE | null | ### Issue you'd like to raise.
import os
from urllib.parse import quote_plus
from langchain.vectorstores.pgvector import PGVector
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.pgvector import DistanceStrategy
os.environ['OPENAI_API_KEY'] = "mykey"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host="192.168.xxx.xx",
port=5432,
database="xxxxx",
user="xxxxxxxxx",
password=quote_plus("xxxx@r"),
)
vectordb = PGVector(embedding_function=embeddings,
collection_name="tmp06",
connection_string=CONNECTION_STRING,
distance_strategy=DistanceStrategy.COSINE,
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 100
retriever = vectordb.as_retriever()
memory = ConversationTokenBufferMemory(
llm=llm,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True
)
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({})
question = input('ask：')
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history)
vectordb.add_vector(res)
best_solution = vectordb.nearest(res, n=1)
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
Traceback (most recent call last):
File "C:\Users\syz\Downloads\ChatBotgpt-3.5-turbo-main\models\1227.py", line 53, in
vectordb.add_vector(res)
^^^^^^^^^^^^^^^^^^^
AttributeError: 'PGVector' object has no attribute 'add_vector'
### Suggestion:
How can I extract vector data from pgvector for use as a reference in the next conversation to enable long-term memory functionality for my chatbot? | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15687 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15687/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15687/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15687/events | https://github.com/langchain-ai/langchain/issues/15687 | 2,069,891,752 | I_kwDOIPDwls57YAqo | 15,687 | No close() functionality in langchain.sql_database import SQLDatabase package | {
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"node_id": "LA_kwDOIPDwls8AAAABUpidyQ",
"url": "https://api.github.com/repos/langchain-ai/langchain/labels/auto:improvement",
"name": "auto:improvement",
"color": "FBCA04",
"default": false,
"description": "Medium size change to existing code to handle new use-cases"
}
] | open | false | null | [] | null | 1 | 2024-01-08T07:38:59 | 2024-01-08T07:41:17 | null | NONE | null | ### Feature request
from langchain_experimental.sql import SQLDatabaseChain
from langchain.sql_database import SQLDatabase
I'm using the above packages to connect the databricks database(SQLDatabse)and passing it to the model chain(SQLDatabaseChain) to generate the SQLQuery. But I want to close the connection of the database after each response. I couldn't find anything to close the database connection using this SQLDatabase package. Even in the SQLDatabase documentation I couldn't find anything. So I need some close() function to close the connection of the database.
### Motivation
Because of this close() functionality not available in the SQLDatabase package. I'm getting (sqlalchemy.exc.OperationalError) so I need to reboot the server to tackle this issue but that was not the feasible solution. And one more thing I can't use other different packages to connect my database because the model chain accept only the SQLDatabase in the parameter.
### Your contribution
Try to add the close() functionality in the SQLDatabase.py file so the database connection can be closed. So that I'll not be facing any issues in the future.
Thanks in advance. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15686 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15686/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15686/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15686/events | https://github.com/langchain-ai/langchain/issues/15686 | 2,069,885,942 | I_kwDOIPDwls57X_P2 | 15,686 | Translate User Query and Model Response in RetrievalQA Chain | {
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] | open | false | null | [] | null | 1 | 2024-01-08T07:34:14 | 2024-01-08T08:19:26 | null | NONE | null | Hi,
I have built a RAG app with RetrievalQA and now wanted to try out a new approach. I am using an English LLM but the responses should be in German. E.g. if the user asks something in German "Hallo, wer bist du?", the user query should be translated to "Hello, who are you?" before feeding it into the RAG pipeline.
After the model made its response in English "I am an helpful assistant" the output should be translated back to German "Ich bin ein hilfreicher Assistent".
As translator I am using `googletrans==3.1.0a0`
Here is my RetrievalQA Chain:
```
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferWindowMemory
import box
import yaml
from src.utils import set_prompt, setup_dbqa, build_retrieval_qa
from src.llm import build_llm
from src.prompts import mistral_prompt
from langchain.vectorstores import FAISS
with open('config/config.yml', 'r', encoding='utf8') as ymlfile:
cfg = box.Box(yaml.safe_load(ymlfile))
def build_retrieval_qaa(llm, prompt, vectordb):
chain_type_kwargs={ "prompt": prompt,
"memory": ConversationBufferWindowMemory(
memory_key="chat_history",
input_key="question",
#output_key="answer",
k=8,
return_messages=True),
"verbose": False
}
dbqa = RetrievalQA.from_chain_type(llm=llm,
chain_type='stuff',
retriever=vectordb,
return_source_documents=cfg.RETURN_SOURCE_DOCUMENTS,
chain_type_kwargs=chain_type_kwargs,
verbose=False
)
return dbqa
llm = build_llm(ANY LLM)
qa_prompt = set_prompt(mistral_prompt)
vectordb = FAISS.load_local(cfg.DB_FAISS_PATH, bge_embeddings)
vectordb = vectordb.as_retriever(search_kwargs={'k': cfg.VECTOR_COUNT, 'score_treshold': cfg.SCORE_TRESHOLD}, search_type="similarity")
dbqa = build_retrieval_qaa(llm, qa_prompt, vectordb)
dbqa("Was bedeutet IPv6 für die Software-Entwicklung?") # Gives me a response
```
The prompt looks like this:
```
mistral_prompt = """
<s> [INST] Du bist RagBot, ein hilfsbereiter Assistent. Antworte nur auf Deutsch. Verwende die folgenden Kontextinformationen, um die Frage am Ende zu beantworten. Wenn du die Antwort nicht kennst, sag einfach, dass du es nicht weisst. Versuche nicht eine Antwort zu erfinden.
###Chat History###: {chat_history}
###Kontext###: {context}
###Frage###: {question}
Antwort: [/INST]
"""
```
So what do I have to change here, to first translate the user query and the prompt from DE to EN, and afterwards the Model response from EN to DE? Specifically I have problems to translate the provided context, chat history and question. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15685 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15685/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15685/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15685/events | https://github.com/langchain-ai/langchain/issues/15685 | 2,069,885,649 | I_kwDOIPDwls57X_LR | 15,685 | Issue: <How can I store 'res' in a vector database, and have a vector retrieval query for the best solution every time there's an input, to achieve long-term memory for OpenAI responses? Please help me modify this string: ' prefix> | {
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] | open | false | null | [] | null | 2 | 2024-01-08T07:33:59 | 2024-01-08T07:55:22 | null | NONE | null | ### Issue you'd like to raise.
import os
from urllib.parse import quote_plus
from langchain.vectorstores.pgvector import PGVector
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationTokenBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores.pgvector import DistanceStrategy
os.environ['OPENAI_API_KEY'] = "mykey"
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
CONNECTION_STRING = PGVector.connection_string_from_db_params(
driver=os.environ.get("PGVECTOR_DRIVER", "psycopg2"),
host="192.168.xxx.xx",
port=5432,
database="xxxxx",
user="xxxxxxxxx",
password=quote_plus("xxxxxr"),
)
vectordb = PGVector(embedding_function=embeddings,
collection_name="tmp06",
connection_string=CONNECTION_STRING,
distance_strategy=DistanceStrategy.COSINE,
)
llm_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=llm_name, temperature=0)
memory_token_limit = 100
retriever = vectordb.as_retriever()
memory = ConversationTokenBufferMemory(
llm=llm,
max_token_limit=int(memory_token_limit),
memory_key="chat_history",
return_messages=True
)
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
memory=memory,
verbose=True,
)
chat_history = []
while True:
memory.load_memory_variables({})
question = input('ask：')
result = qa.run({'question': question, 'chat_history': chat_history})
print(result)
chat_history.append([f'User: {question}', f'Ai: {result}'])
print(chat_history)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
st_history = ' '.join(map(str, chat_history))
res = embeddings.embed_query(st_history) ########################ˇ
print(f'ok: {res[:4]}...')
if question.lower() == 'bye':
break
How can I store 'res' in a vector database, and have a vector retrieval query for the best solution every time there's an input, to achieve long-term memory for OpenAI responses? Please help me modify this string translates to "Please help me see if there are any errors" in English
### Suggestion:
How can I store 'res' in a vector database, and have a vector retrieval query for the best solution every time there's an input, to achieve long-term memory for OpenAI responses? Please help me modify this string translates to "Please help me see if there are any errors" in English | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15684 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15684/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15684/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15684/events | https://github.com/langchain-ai/langchain/issues/15684 | 2,069,861,793 | I_kwDOIPDwls57X5Wh | 15,684 | How to use tools for tasks that are dependent on each other | {
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] | open | false | null | [] | null | 1 | 2024-01-08T07:14:13 | 2024-01-08T07:24:05 | null | NONE | null | ### Issue you'd like to raise.
User: Help me reset my password
Agent: Please provide your account number
User: My account is Axxx
Agent: SMS verification code has been sent, please provide SMS verification code
User: 091839
Agent: Account password has been reset to 123456
The Agent is responsible for resetting the password for the user. In this example, the Agent needs to communicate back and forth with the user, such as providing an account to send a verification code, and providing a verification code to reset the password.
I added 4 tools, but when I asked any questions, all the tools were used once, which was not what I expected. I wanted to use the corresponding tool according to the specific situation.
```
> Entering new AgentExecutor chain...
{
"action": "ResetPasswordAskTool",
"action_input": "Axxx"
}
Observation: 请提供下您的账号：
Thought:/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:189: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(
{
"action": "ReceiveUserAccountTool",
"action_input": "Axxx"
}
Observation: 已经接收到您的账号，您提供的账号为：Axxx
Thought:/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:189: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(
{
"action": "SendSmsTool",
"action_input": "Axxx"
}
Observation: 短信验证码已发出，请查看手机收到的重置密码的短信验证码，并提供给我。
Thought:/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:189: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(
{
"action": "ResetPasswordTool",
"action_input": "123456"
}
Observation: 密码已经重置为：123321
Thought:/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:189: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(
{
"action": "Final Answer",
"action_input": "密码已经重置为：123321"
}
> Finished chain.
intermediate_steps= [(AgentAction(tool='ResetPasswordAskTool', tool_input='Axxx', log='{\n "action": "ResetPasswordAskTool",\n "action_input": "Axxx"\n}'), '请提供下您的账号：'), (AgentAction(tool='ReceiveUserAccountTool', tool_input='Axxx', log='{\n "action": "ReceiveUserAccountTool",\n "action_input": "Axxx"\n}'), '已经接收到您的账号，您提供的账号为：Axxx'), (AgentAction(tool='SendSmsTool', tool_input='Axxx', log='{\n "action": "SendSmsTool",\n "action_input": "Axxx"\n}'), '短信验证码已发出，请查看手机收到的重置密码的短信验证码，并提供给我。'), (AgentAction(tool='ResetPasswordTool', tool_input='123456', log='{\n "action": "ResetPasswordTool",\n "action_input": "123456"\n}'), '密码已经重置为：123321')]
response output= 密码已经重置为：123321
```
### Suggestion:
_No response_ | {
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- **Issue:** #12710
- **Dependencies:** N/A,
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] | closed | false | null | [] | null | 4 | 2024-01-08T05:19:09 | 2024-01-08T05:43:30 | 2024-01-08T05:43:18 | NONE | null | ### System Info
Langchain 0.1.0
Python 3.10.12
### Who can help?
@hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents.agent_toolkits import O365Toolkit
otoolkit = O365Toolkit()
o365_tools = otoolkit.get_tools()
tools.append(o365_tools)
from langchain_experimental.openai_assistant import OpenAIAssistantRunnable
agent = OpenAIAssistantRunnable.create_assistant(
name="My assistant",
# instructions="""You are a admin agent, tasked with the following jobs:
1. Read and post messages on Microsoft 365 Outlook"""
tools=tools,
model="gpt-4-1106-preview",
as_agent=True
)
from langchain.agents.agent import AgentExecutor
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
from langchain.agents.agent import AgentExecutor
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
-------------------------------------------------------------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[13], line 3
1 from langchain.agents.agent import AgentExecutor
----> 3 agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools)
File ~/smith/lib/python3.10/site-packages/langchain/agents/agent.py:891, in AgentExecutor.from_agent_and_tools(cls, agent, tools, callbacks, **kwargs)
882 @classmethod
883 def from_agent_and_tools(
884 cls,
(...)
888 **kwargs: Any,
889 ) -> AgentExecutor:
890 """Create from agent and tools."""
--> 891 return cls(
892 agent=agent,
893 tools=tools,
894 callbacks=callbacks,
895 **kwargs,
896 )
File ~/smith/lib/python3.10/site-packages/langchain_core/load/serializable.py:107, in Serializable.__init__(self, **kwargs)
106 def __init__(self, **kwargs: Any) -> None:
--> 107 super().__init__(**kwargs)
108 self._lc_kwargs = kwargs
File ~/smith/lib/python3.10/site-packages/pydantic/main.py:339, in pydantic.main.BaseModel.__init__()
File ~/smith/lib/python3.10/site-packages/pydantic/main.py:1102, in pydantic.main.validate_model()
File ~/smith/lib/python3.10/site-packages/langchain/agents/agent.py:916, in AgentExecutor.validate_return_direct_tool(cls, values)
914 """Validate that tools are compatible with agent."""
915 agent = values["agent"]
--> 916 tools = values["tools"]
917 if isinstance(agent, BaseMultiActionAgent):
918 for tool in tools:
KeyError: 'tools'
### Expected behavior
agent_executor should get created properly, this was working a week ago. | {
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```sql
DELETE FROM collection WHERE id = '1' AND id = '2' AND id = '3'
```
But the expected one should be
```sql
DELETE FROM collection WHERE id IN ('1', '2', '3')
``` | {
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