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https://api.github.com/repos/langchain-ai/langchain/issues/15232
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| 2,057,799,134
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I_kwDOIPDwls56p4Xe
| 15,232
|
HuggingFace Chat Wrapper - issue with HuggingFaceHub
|
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| 2023-12-27T22:02:49
| null |
NONE
| null |
### Issue with current documentation:
I am currently following the document to use a hugigngface LLM as a chat model: https://python.langchain.com/docs/integrations/chat/huggingface
I have setup my Huggingface API and am using Option 3 (HuggingFaceHub) to instantiate an LLM.
After running this line: chat_model._to_chat_prompt(messages)
I get the following error: ValueError: last message must be a HumanMessage
I am running the code the exactly the same as the documentation, including using the HuggingFaceH4/zephyr-7b-beta model.
Any help in resolving this issue is much appreciated.
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5i2bQy
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refactored `output_parsers`
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| 2023-12-27T21:01:42
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|
COLLABORATOR
| null |
Several output parsers belong to the `langchain_community` package.
- Created `langchain_community.output_parsers` namespace
- Moved several parsers to community package
|
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PR_kwDOIPDwls5i2Wcw
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ModelIO revamp
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5i2Njp
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Add .pick and .assign methods to Runnable
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|
COLLABORATOR
| 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.
-->
|
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I_kwDOIPDwls56pezB
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TypeError: _ChatSessionBase.send_message() got an unexpected keyword argument 'candidate_count'
|
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| 2023-12-27T19:12:44
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NONE
| null |
### Issue you'd like to raise.
@dosu-bot Currently im experiencing an old bug that was supposed to be fixed patches ago.
```
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/flask/app.py", line 1455, in wsgi_app
response = self.full_dispatch_request()
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/flask/app.py", line 869, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/flask/app.py", line 867, in full_dispatch_request
rv = self.dispatch_request()
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/flask/app.py", line 852, in dispatch_request
return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args)
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/functions_framework/__init__.py", line 134, in view_func
return function(request._get_current_object())
File "/workspace/main.py", line 109, in entry_point_http
faq_response = chain.invoke(inputs)
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1510, in invoke
input = step.invoke(
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 160, in invoke
self.generate_prompt(
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 491, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 378, in generate
raise e
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 368, in generate
self._generate_with_cache(
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 524, in _generate_with_cache
return self._generate(
File "/layers/google.python.pip/pip/lib/python3.10/site-packages/langchain/chat_models/vertexai.py", line 187, in _generate
response = chat.send_message(question.content, **msg_params)
TypeError: _ChatSessionBase.send_message() got an unexpected keyword argument 'candidate_count'
```
My current version is 0.0.348 and im trying to create a Cloud Function. Here is my code:
```
from google.cloud import bigquery, storage
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.chat_models import ChatVertexAI
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.memory import ConversationSummaryBufferMemory
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from operator import itemgetter
from langchain.schema.output_parser import StrOutputParser
from langchain.callbacks.tracers import ConsoleCallbackHandler
from langchain.embeddings import VertexAIEmbeddings
from langchain.llms import VertexAI
from langchain.prompts import PromptTemplate, ChatPromptTemplate
from langchain.retrievers import BM25Retriever, EnsembleRetriever, ContextualCompressionRetriever
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain.document_transformers import EmbeddingsRedundantFilter
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain.schema import Document
from langchain.schema import StrOutputParser
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
import google.cloud.storage
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.prompts import StringPromptTemplate
from typing import List, Union
from langchain.prompts import StringPromptTemplate
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from langchain.vectorstores import MatchingEngine
import re
import io
import ipywidgets as widgets
import json
import langchain
import math
import os
import pandas as pd
import time
import logging
from faq_redpro_prompt import template_faq
from faq_redpro_fewshot import few_shot_faq
PROJECT_ID_ME = os.environ.get("PROJECT_ID_ME")
ME_REGION = os.environ.get("ME_REGION")
ME_BUCKET_FAQ = os.environ.get("ME_BUCKET_FAQ")
ME_INDEX_ID_FAQ = os.environ.get("ME_INDEX_ID_FAQ")
ME_INDEX_ENDPOINT_ID_FAQ = os.environ.get("ME_INDEX_ENDPOINT_ID_FAQ")
def entry_point_http(request):
request_json = request.get_json()
# Extraer la entrada del parámetro enviado por Dialogflow CX
user_query = request_json.get('sessionInfo', {}).get('parameters', {}).get('user_query')
#Modelos
llm = VertexAI(
model_name = "text-bison",
temperature = 0.1 #Prueba
)
chat = ChatVertexAI(
model_name = "chat-bison@001",
temperature = 0.4,
top_p = 0.8,
top_k = 40,
max_output_tokens = 500
)
embeddings = VertexAIEmbeddings(model_name="textembedding-gecko-multilingual@001")
me_faqs = MatchingEngine.from_components(
project_id=PROJECT_ID_ME,
region=ME_REGION,
gcs_bucket_name=ME_BUCKET_FAQ,
embedding=embeddings,
index_id=ME_INDEX_ID_FAQ,
endpoint_id=ME_INDEX_ENDPOINT_ID_FAQ,
)
me_retriever = me_faqs.as_retriever(
search_type="similarity",
search_kwargs={
"k": 2,
},
)
faq_prompt = PromptTemplate(
template=template_faq,
input_variables=["context", "question", "few_shot_faq"]
)
chain = (
RunnablePassthrough.assign(
context=itemgetter("question") | me_retriever,
question=itemgetter("question"),
few_shot_faq=itemgetter("few_shot_faq"),
)
| faq_prompt
| chat
| StrOutputParser()
)
inputs = {"question": user_query, "few_shot_faq": few_shot_faq}
faq_response = chain.invoke(inputs)
print(f'LangChain response: {faq_response}')
formatted_results = format_response(faq_response)
response["fulfillment_response"]["messages"][0]["text"]["text"][0] = formatted_results
return (response, 200, headers)
def format_response(results):
answer = results['answer']
sources = results.get('sources', '')
if sources != '':
source_uri = sources
else:
source_documents = results.get('source_documents', '')
if source_documents != '':
source_uri = results['source_documents'][0].metadata['source']
else:
source_uri = 'Não encontrei uma fonte para essa pergunta.'
formatted_response = f"{answer}\nSources: {source_uri}"
return formatted_response
```
### Suggestion:
_No response_
|
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| 2,057,681,667
|
I_kwDOIPDwls56pbsD
| 15,227
|
Issue: issue with pdfplumber
|
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open
| false
| null |
[] | null | 6
| 2023-12-27T18:50:09
| 2023-12-28T06:42:13
| null |
NONE
| null |
### Issue you'd like to raise.
from langchain.document_loaders.parsers.pdf import PDFPlumberParser
def generate_embeddings(config: dict = None, urls = None, file_path = None, persist_directory=None):
if file_path:
parser = PDFPlumberParser()
data = parser.load(file_path)
processed_data = parser.process(data)
print(processed_data,"processed_data is-----------------llllllllllllllllllllllllllllll")
#below is the error i'm getting
data = parser.load(file_path)
AttributeError: 'PDFPlumberParser' object has no attribute 'load'
### Suggestion:
_No response_
|
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https://github.com/langchain-ai/langchain/pull/15226
| 2,057,675,477
|
PR_kwDOIPDwls5i2G6F
| 15,226
|
Implement nicer runnable seq constructor, Propagate name through Runn…
|
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[] | null | 1
| 2023-12-27T18:41:39
| 2023-12-27T19:24:33
| 2023-12-27T19:24:32
|
COLLABORATOR
| null |
…ableBinding
<!-- 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.
-->
|
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| 2,057,606,755
|
PR_kwDOIPDwls5i14Jy
| 15,223
|
core + community: Added ANN override to VectorStore [new feature]
|
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[] | null | 4
| 2023-12-27T17:14:55
| 2023-12-28T13:36:58
| 2023-12-28T13:33:54
|
NONE
| null |
Addresses: https://github.com/langchain-ai/langchain/issues/15221
(Most of the context of this PR exists on that issue.)
Adds this method to VectorStore:
```
@abstractmethod
def similarity_search_with_override(
self, query: str, search_override: str, k: int = 4, **kwargs: Any
) -> List[Document]:
"""Return docs most similar to query."""
```
Updates _get_relevant_documents() on VectorStore to include search_override, like this:
```
elif self.search_type == "similarity_with_override":
docs = self.vectorstore.similarity_search_with_override(query, self.search_override, **self.search_kwargs)
```
Also adds the corresponding method to AstraDB (as an example):
```
def similarity_search_with_override(
self,
query: str,
search_override: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
embedding_vector = self.embedding.embed_query(search_override)
return self.similarity_search_by_vector(
embedding_vector,
k,
filter=filter,
)
```
When I try to run the tests from root, I get:
```
% poetry install --with test
Installing dependencies from lock file
No dependencies to install or update
% make test
make: *** No rule to make target `test'. Stop.
```
When I try to run the tests from core, I get:
```
% make test
poetry run pytest tests/unit_tests/
ERROR: usage: pytest [options] [file_or_dir] [file_or_dir] [...]
pytest: error: unrecognized arguments: --snapshot-warn-unused
inifile: /Users/devin.bost/proj/repos/langchain/libs/core/pyproject.toml
rootdir: /Users/devin.bost/proj/repos/langchain/libs/core
make: *** [test] Error 4
```
I tried the steps in the [guide](https://python.langchain.com/docs/contributing/testing), but I'm still not getting tests to run. Some guidance would be much appreciated.
I'd also appreciate some guidance on how I might test this new functionality in LCEL.
|
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| 2,057,600,249
|
I_kwDOIPDwls56pHz5
| 15,222
|
support gemini on vertexai
|
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open
| false
| null |
[] | null | 9
| 2023-12-27T17:07:05
| 2024-01-17T12:43:47
| null |
NONE
| null |
### Feature request
As per documentation there's a package for Gemini support but this only works for Gemini API and doesn't work with Vertexai.
https://python.langchain.com/docs/integrations/platforms/google
However in the vertexai docs gemini is mentioned (for some reason gemini ultra ? ) even though when tried with geimini-pro (gemini-ultra is not out yet unless Langchain folks have connections at Google :) ) it's throwing an error indicating that model doesn't exist.
https://python.langchain.com/docs/integrations/llms/google_vertex_ai_palm#multimodality
Unknown model publishers/google/models/gemini-pro-vision; {'gs://google-cloud-aiplatform/schema/predict/instance/chat_generation_1.0.0.yaml': <class 'vertexai.language_models.ChatModel'>} (type=value_error)
### Motivation
gemini has been out for a while and seemingly should be supported by langchain as they already made a whole package for it.
### Your contribution
I would be willing to make a pr but I'm not even sure what's the issue since the docs supposedly mention that it should be already supported.
|
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| 2,057,589,837
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I_kwDOIPDwls56pFRN
| 15,221
|
Enable manual override of vector search query for controlled RAG
|
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| false
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| 2023-12-27T16:54:54
| 2023-12-27T16:57:11
| null |
NONE
| null |
### Feature request
I need a mechanism to allow more control over the ANN search performed for a given RAG chain. Consider the initial example:
```
retriever = vectorstore.as_retriever()
template = """You're a helpful assistant who is great at code generation. Don't give me any explanation or summary. I'll give you some examples that may or may not be relevant, and I want you to use the examples to write code that solves the provided problem. Return only the code that solves the problem.
PROBLEM:
{problem}
EXAMPLES:
{context}
ANSWER:
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI()
chain = (
{"context": retriever, "problem": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
chain.invoke("Generate an example Python method that uses LCEL to write a CQL query")
```
This approach assumes that the question will be used for creating the embedding. However, consider something like this:
```
retriever = vectorstore.as_retriever(ann_query="LCEL, AstraDB, CQL")
```
In this situation, when the retriever is invoked to embed the query, instead of performing vector search on the embedding of the very wordy
> "Generate an example Python method that uses LCEL to write a CQL query"
I want vector search to perform ANN on:
> "LCEL, AstraDB, CQL"
so that I have a greater likelihood of having the right docs stuffed into the prompt for the LLM to solve the problem, which was:
> Generate an example Python method that uses LCEL to write a CQL query
### Motivation
RAG results can be poor when the human input is very wordy or contains more info (for the LLM) than we want the vector store to search for. We need a mechanism to allow separation between the vector search query and the LLM query.
### Your contribution
I will create a PR.
|
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I_kwDOIPDwls56pCuu
| 15,220
|
Langchain agent not executing properly in Celery worker running as Docker container
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[] | null | 5
| 2023-12-27T16:42:21
| 2024-01-02T19:26:40
| 2024-01-02T19:26:40
|
NONE
| null |
### System Info
Langchain version: 0.0.341
OpenAI version: 1.3.5
Model: gpt-4-1106-preview
Python version:3.10.13
Platform: Celery worker in Docker Container
### Who can help?
@eyurtsev @hwchase17 @ag
### 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
I am working on implementing LangChain Agents in my Python project I am running this project using docker compose. In my project I am using Celery worker and have multiple worker services which executes from the queue. The entire setup is working fine and all celery tasks are executed as expected.
One of these workers in agent worker where I have configured LangChain Agent. I have created a function where I am loading tools, initializing agent and passing agent input. Here's the full code of my **agent module**:
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| 2023-12-27T16:30:50
| 2024-01-08T21:16:01
| null |
CONTRIBUTOR
| null |
- **Description:** Following a private discussion with @rlancemartin and @SandraKublik, this PR adds a new template for RAG Conversation using Cohere Embed v3 and Cohere Chat.
This template is very similar to the [Rag Conversation template](https://github.com/langchain-ai/langchain/tree/master/templates/rag-conversation) which uses OpenAI's API instead for the embeddings and chat endpoints.
I have tested it locally and it works as expected.
If this PR gets merged, we may want to rename the Rag Conversation template to specify that OpenAI is used for the embeddings and chat (for instance to something like: `rag-conversation-openai`).
- **Issue:** N/A
- **Dependencies:** No dependencies added for this change.
- **Twitter handle:** [@smlpth](https://twitter.com/smlpth)
<!-- 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.
-->
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google-vertexai: added langchain_google_vertexai package
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CONTRIBUTOR
| null |
added langchain_google_vertexai package
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CONTRIBUTOR
| null |
- **Description:** Following a private discussion with @rlancemartin and @SandraKublik, this PR adds a new template using:
- Cohere Embed v3
- Cohere Chat
- Cohere Re-Rank
This template is very similar to the [Rag Pinecone Rerank template](https://github.com/langchain-ai/langchain/blob/master/templates/rag-pinecone-rerank) which uses OpenAI's API instead for the embeddings and chat endpoints.
I have tested it locally and it works as expected.
If this PR gets merged, we may want to rename the Rag Pinecone Rerank template to specify that OpenAI is used for the embeddings and chat (for instance to something like: `rag-pinecone-openai-embed-rerank`).
- **Issue:** N/A
- **Dependencies:** No dependencies added for this change.
- **Twitter handle:** [@smlpth](https://twitter.com/smlpth)
<!-- 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.
-->
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how to create a custom chat model
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[] | null | 2
| 2023-12-27T13:27:25
| 2023-12-27T13:29:31
| null |
NONE
| null |
### Issue with current documentation:
Like title, haven't found anything in the doc - docubot please help
### Idea or request for content:
having a proper document could help
|
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| 15,213
|
How to query with new variables in LCEL mode?
|
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[] | null | 3
| 2023-12-27T12:51:07
| 2023-12-27T15:29:26
| null |
NONE
| null |
### System Info
I am plannign to add new param like "affeciton"
How could I set the query databody to fill up the params here?( Langserve setup !)
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
How to handle the input variables in LCEL mode?
### Expected behavior
get the right place I put in when I new a variables in the prompt.
|
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PR_kwDOIPDwls5i074W
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community: avoid KeyError when language not in LANGUAGE_SEGMENTERS
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| 2023-12-27T12:42:43
| 2024-01-05T23:25:28
| null |
NONE
| null |
**Description:**
Handle unsupported languages in same way as when none is provided
**Issue:**
The following line will throw a KeyError if the language is not supported.
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self.Segmenter = LANGUAGE_SEGMENTERS[language]
```
E.g. when using `Language.CPP` we would get `KeyError: <Language.CPP: 'cpp'>`
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PR_kwDOIPDwls5i0wwU
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|
community: Redis chat history with token limit
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| 2024-01-04T15:18:18
| null |
NONE
| null |
- **Description:** added new class for redis chat message history with a token limit
- **Dependencies:** None
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I_kwDOIPDwls56n5Dx
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Error using ConversationalRetrievalChain.from_llm: "document_variable_name context was not found in llm_chain input_variables: [] (type=value_error)"
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| 2023-12-27T11:46:08
| 2023-12-27T11:55:50
| null |
NONE
| null |
### System Info
MacOS, M1 Pro
### Who can help?
@hwchase17 @agola11
### Information
- [X] 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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Run the following code:
```
import os
from dotenv import load_dotenv
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.chat_models import ChatOllama
from langchain.vectorstores import FAISS
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings.openai import OpenAIEmbeddings
load_dotenv()
messages = [
SystemMessagePromptTemplate.from_template(
"You are a truthful, accurate AI agent that responds to the user's questions, given an AI paper by Apple."
),
HumanMessagePromptTemplate.from_template("What is the paper about, in summary?"),
]
qa_prompt = ChatPromptTemplate.from_messages(messages)
chat_model = ChatOllama(
model="mistral",
)
loader = PyPDFLoader("./llm_in_a_flash_apple.pdf")
pages = loader.load_and_split()
embeddings = OpenAIEmbeddings(api_key=os.getenv("OPENAI_API_KEY"))
print(pages[0])
db = None
if not os.path.exists("./faiss_index"):
db = FAISS.from_documents(pages, embeddings)
db.save_local("./faiss_index")
else:
db = FAISS.load_local("faiss_index", embeddings)
query = "What is the paper about?"
docs = db.similarity_search_with_score(query)
print(docs[0])
ConversationalRetrievalChain.from_llm(
llm=chat_model,
retriever=db.as_retriever(search_type="similarity", search_kwargs={"k": 0.8}),
verbose=True,
combine_docs_chain_kwargs={"prompt": qa_prompt},
return_source_documents=True,
)
```
additional files here: https://github.com/polooner/chatpdf/blob/main/main.py
### Expected behavior
An answer from the Chat Model
|
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| 2,057,237,873
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PR_kwDOIPDwls5i0nrp
| 15,209
|
Add missing comment char "#" before Load in chain.py for the rag-pinecone-rerank template
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| 2023-12-27T11:08:55
| 2024-01-01T22:01:07
| 2024-01-01T22:01:07
|
CONTRIBUTOR
| null |
Without this additional `#`, one needs to add it manually after uncommenting the section.
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PR_kwDOIPDwls5i0djV
| 15,207
|
community: Fix Baichuan Chat.
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open
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| null |
[] | null | 7
| 2023-12-27T10:22:39
| 2024-01-11T04:09:55
| null |
NONE
| null |
- **Description:** Baichuan Chat (with both Baichuan-Turbo and Baichuan-Turbo-192K models) has updated their APIs. There are breaking changes. For example, BAICHUAN_SECRET_KEY is removed in the latest API but is still required in Langchain. Baichuan's Langchain integration needs to be updated to the latest version.
- **Issue:** #15206
- **Dependencies:** None,
- **Twitter handle:** None
@hwchase17.
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I_kwDOIPDwls56njt6
| 15,206
|
Fix Baichuan's integration and introduce Baichuan-Turbo-192K API.
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open
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| null |
[] | null | 1
| 2023-12-27T10:21:21
| 2023-12-27T10:29:44
| null |
NONE
| null |
### System Info
Baichuan Chat (with both Baichuan-Turbo and Baichuan-Turbo-192K models) has updated their APIs. There are breaking changes. For example, BAICHUAN_SECRET_KEY is removed in the latest API but is still required in Langchain. Baichuan's Langchain integration needs to be updated to the latest version.
Also we have released out new Baichuan-Turbo-192K API. We are adding support for this.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
https://python.langchain.com/docs/integrations/chat/baichuan
SECRET_KEY has been deprecated.
### Expected behavior
Baichuan Chat works normally.
|
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I_kwDOIPDwls56njf-
| 15,205
|
Issue: Streaming Response contains the rephrased question in ConversationalRetrievalChain
|
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[] | null | 3
| 2023-12-27T10:20:47
| 2023-12-27T10:59:28
| null |
NONE
| null |
### Issue you'd like to raise.
I am using ConversationalRetrievalChain
with a callback handler for streaming responses back.
> qa_chain =ConversationalRetrievalChain.from_llm(
llm=chat,
retriever=MyVectorStoreRetriever(
vectorstore=vectordb,
search_type="similarity_score_threshold",
search_kwargs={"score_threshold": SIMILARITY_THRESHOLD, "k": 1},
),
return_source_documents=True,
rephrase_question=False,
return_generated_question=False,
)
> response = qa_chain(
{
"question": user_input,
"chat_history": chat_history,
},
callbacks=[stream_handler],
)
```
class StreamHandler(BaseCallbackHandler):
def __init__(self):
self.text = ""
def on_llm_new_token(self, token: str, **kwargs: Any):
# Initialize old_text
old_text = self.text
print("old text ", old_text)
# Check if the token is not part of the prompts before adding it to the queue
print("token is", token)
if token is not None and token != "":
self.text += token
# Calculate the new content since the last emission
new_content = self.text[len(old_text) :]
socketio.emit("update_response", {"response": new_content})
```
I have provided the value of rephrase_question and return_generated_question False.
Even after that the streaming response contains the rephrased question.
But the final response from the LLM does not contain this rephrased question.
what could be the reason, please provide an appropriate solution.
### Suggestion:
_No response_
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I_kwDOIPDwls56nhI_
| 15,204
|
Pydantic forward ref issue when creating using LlamaCpp with grammar
|
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[] | null | 1
| 2023-12-27T10:11:11
| 2023-12-27T10:13:28
| null |
NONE
| null |
### System Info
OS: MacOS Sonoma
Python: 3.11.6
LangChain: 0.0.352
llama-cpp-python = 0.2.25
pydantic: 1.10.13 (I know that it is not the latest version, but version 1 is still officially supported)
### Who can help?
@hwchase17
@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
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
When trying to use LlamaCpp in conjunction with grammar, I get an error from pydantic. The following code snipped was adapted from the [docs](https://python.langchain.com/docs/integrations/llms/llamacpp#grammars): so that a `LlamaGrammar` is passed, instead of the path to the grammar file.
```python
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms.llamacpp import LlamaCpp
from llama_cpp.llama_grammar import LlamaGrammar
from pydantic import BaseModel
class SomeSchema(BaseModel):
some_field: str
LlamaCpp(
model_path="some model",
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
grammar=LlamaGrammar.from_json_schema(SomeSchema.schema_json()),
)
# Fails with:
# pydantic.errors.ConfigError: field "grammar" not yet prepared so type is still a ForwardRef, you might need to call LlamaCpp.update_forward_refs().
```
The following works though (and the grammar object is used properly:
```python
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms.llamacpp import LlamaCpp
from llama_cpp.llama_grammar import LlamaGrammar
from pydantic import BaseModel
class SomeSchema(BaseModel):
some_field: str
model = LlamaCpp(
model_path="some model",
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
)
model.grammar = LlamaGrammar.from_json_schema(SomeSchema.schema_json())
```
### Expected behavior
It should be possible to pass a `LlamaGrammar` object in the `__init__` of `LlamaCpp`, as per its [definition](https://github.com/langchain-ai/langchain/blob/f36ef0739dbb548cabdb4453e6819fc3d826414f/libs/community/langchain_community/llms/llamacpp.py#L129)
I had a quick look at the pydantic [documentation regarding this problem](https://docs.pydantic.dev/1.10/usage/postponed_annotations/), but I couldn't find the postponed annotation in question.
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Issue: Identical Content in Two Files
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| 2023-12-27T09:49:59
| 2023-12-27T09:56:42
| null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/tools/render.py
https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/tools/convert_to_openai.py
For backward compatibility purposes, should we proceed with a direct import?
### Suggestion:
_No response_
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PR_kwDOIPDwls5i0QaH
| 15,202
|
Fix typo
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|
CONTRIBUTOR
| null |
Small typo fix in the templates docs: `languge` -> `language`
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|
"Could not parse LLM output" when using QianfanChatEndpoint in agent.
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[] | null | 2
| 2023-12-27T08:49:02
| 2023-12-28T17:28:50
| null |
NONE
| null |
### System Info
langchian=0.0.352
qianfan=0.2.4
When I tried the usage of agent in this [video](https://learn.deeplearning.ai/langchain/lesson/7/agents), I changed the model in it from ChatGpt-3.5-turbo to ERNIE-Bot, and the output of agent showed the following error:
```bash
> Entering new AgentExecutor chain...
Could not parse LLM output: xxxxxxxxx
Observation: Invalid or incomplete response
Thought: Could not parse LLM output: xxxxx
Observation: Invalid or incomplete response
...
```
And, ERNIE-Bot can't call (llm-math) tool correctly.
I wonder if the problem is a lack of capability in the qianfan model itself, or if there is a problem in the qianfan code.
Or is there something wrong with my usage or other issues?
### Who can help?
@danielhjz
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**my code**
```python
llm = QianfanChatEndpoint(
temperature=0.000001,
model='ERNIE-Bot'
)
tools = load_tools(
["llm-math", "wikipedia"],
llm=llm
)
agent = initialize_agent(
tools,
llm,
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
verbose=True
)
agent("300的1/4是多少？")
```
**code in the video**
```python
# code in the video
llm = ChatOpenAI(
temperature=0
)
tools = load_tools(
["llm-math", "wikipedia"],
llm=llm
)
agent = initialize_agent(
tools,
llm,
agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
verbose=True
)
agent("What is the 25% of 300?")
```
### Expected behavior
**Run by ChatOpenAI(temperature=0)**
````bash
> Entering new AgentExecutor chain...
Thought: We need to calculate 25% of 300, which means we need to multiply 300 by 0.25.
Action:
```
{
"action": "Calculator",
"action_input": "300*0.25"
}
```
Observation: Answer: 75.0
Thought:The calculator tool returned the answer 75.0, which is correct.
Final Answer: 25% of 300 is 75.0.
> Finished chain.
{'input': 'What is the 25% of 300?', 'output': '25% of 300 is 75.0.'}
````
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| 2,057,089,303
|
PR_kwDOIPDwls5i0HJL
| 15,198
|
community: Fix inconsistent initialization for ChatTongyi
|
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| 2024-01-02T08:04:57
| 2024-01-01T22:32:16
|
NONE
| null |
## Description:
When using ChatTongyi, specifying the "model_name" parameter does not take effect (only the "model" parameter works). This inconsistency with other chat models can lead to unexpected model calls.
### Demonstration:
```Python
In[1]: DASHSCOPE_API_KEY = "sk-xxx"
In[2]: from langchain.chat_models.tongyi import ChatTongyi
In[3]: tongyi_model = ChatTongyi(model_name="qwen-plus",dashscope_api_key=DASHSCOPE_API_KEY)
In[4]: tongyi_model.model_name
Out[5]: 'qwen-turbo' # expected 'qwen-plus'
```
However, other chat models like ChatOpenAI can be assigned using the "model_name" parameter, which makes the behavior of the ChatTongyi model inconsistent.
```python
In[6]: from langchain.chat_models import ChatOpenAI
In[7]: open_ai_model = ChatOpenAI(openai_api_key="sk-xxx", model_name="gpt-4")
In[8]: open_ai_model.model_name
Out[9]: 'gpt-4'
```
## How to fix:
Set pydantic configuration `allow_population_by_field_name=True`
@baskaryan
|
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Patch: improve openai functions call parser compatibility
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| 2023-12-28T23:06:27
|
CONTRIBUTOR
| null |
```shell
Python 3.11.6 (main, Nov 2 2023, 04:39:43) [Clang 14.0.3 (clang-1403.0.22.14.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> s = {'name': 'gc', 'arguments': '{"prompt":"hi\nbob."}'}
>>> import json
>>> json.loads(s['arguments'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
^^^^^^^^^^^^^^^^^^^^^^
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 14 (char 13)
>>> json.loads(s['arguments'].replace('\n', '\\n'))
{'prompt': 'hi\nbob.'}
>>>
```
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Fixed minor type in self_query.ipynb
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|
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.
-->
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I_kwDOIPDwls56mfdr
| 15,195
|
Issue: lcel langserve with_fallbacks streaming
|
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| 2023-12-27T04:53:43
| 2024-01-13T07:00:58
| null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
```python
conversational_qa_chain = (
_inputs | _context | ConfigurableTokenLimitProcessor(model="gpt_35_turbo").configurable_fields(
model=ConfigurableFieldSingleOption(
id="model",
name="model",
options={
"gpt_35_turbo": "gpt_35_turbo",
"gpt_35_turbo_1106": "gpt_35_turbo_1106",
"gpt_4_1106_preview": "gpt_4_1106_preview",
"gpt_4_32k": "gpt_4_32k"
},
default="gpt_35_turbo",
)
) | ANSWER_PROMPT | llm | StrOutputParser()
)
```
```python
chain = conversational_qa_chain.with_types(input_type=ChatHistory).with_fallbacks([RunnableLambda(when_all_is_lost)])
```
```python
add_routes(app,
chain,
enable_feedback_endpoint=True,
path="/test",
config_keys=["llm", "collection_name", "model", "configurable"]
)
```
It's a code developed with langserve, but if i send a request to `/test/stream` using playground, unlike before adding the `with_fallbacks` function, the response is not exposed on the screen by token, but all the responses are shown on the screen at once, what's the reason?
### Suggestion:
Even if i add `with_fallbacks`, it should be streamed on the screen for each token.
|
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| 2,056,898,273
|
I_kwDOIPDwls56mcbh
| 15,193
|
Issue: <Please tell me how to combine Routing and RAG in a chain.>
|
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| 2023-12-27T04:29:43
| 2024-01-09T03:06:19
| null |
NONE
| null |
### Issue you'd like to raise.
I apologize for the naive question.it's not about an error or a bug.
I'm trying to implement routing by following the guide here: https://python.langchain.com/docs/modules/chains/foundational/router
However, I can't figure out how to use RAG.
I tried changing the last code in the guide like this.
```python
final_chain = (
RunnablePassthrough.assign(topic=itemgetter("input") | classifier_chain)
| prompt_branch
| ChatOpenAI()
| StrOutputParser()
)
```
```python
final_chain = (
{
"context": retriever,
"topic": itemgetter("input") | classifier_chain,
}
| prompt_branch
| llm
| StrOutputParser()
)
```
But I get the following error:
```shell
File "/Users/user/Library/Python/3.9/lib/python/site-packages/tiktoken/core.py", line 116, in encode
if match := _special_token_regex(disallowed_special).search(text):
TypeError: expected string or buffer
```
### Suggestion:
_No response_
|
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reopened
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I_kwDOIPDwls56mYSX
| 15,192
|
Issue: <CSVLoader can't load>
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[] | null | 5
| 2023-12-27T03:54:38
| 2024-01-09T09:47:57
| 2024-01-08T04:10:00
|
NONE
| null |
### Issue you'd like to raise.
why CSVLoader can't load? error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[25], line 1
----> 1 from langchain.document_loaders.csv_loader import CSVLoader
3 loader = CSVLoader(file_path='./data/bugreport.csv', csv_args={
4 'delimiter': ',',
5 'quotechar': '"',
6 'fieldnames': ["URL","Resolved","Backport_of","Submitted","Status","CPU","Priority","Sub_Component","Updated","Fix_Versions","Affected_Version","OS","Type","Resolution","Component"]
7 })
9 data = loader.load()
File D:\miniconda\lib\site-packages\langchain\document_loaders\__init__.py:49
47 from langchain.document_loaders.bigquery import BigQueryLoader
48 from langchain.document_loaders.bilibili import BiliBiliLoader
---> 49 from langchain.document_loaders.blackboard import BlackboardLoader
50 from langchain.document_loaders.blob_loaders import (
51 Blob,
52 BlobLoader,
53 FileSystemBlobLoader,
54 YoutubeAudioLoader,
55 )
56 from langchain.document_loaders.blockchain import BlockchainDocumentLoader
File D:\miniconda\lib\site-packages\langchain\document_loaders\blackboard.py:1
----> 1 from langchain_community.document_loaders.blackboard import BlackboardLoader
3 __all__ = ["BlackboardLoader"]
File D:\miniconda\lib\site-packages\langchain_community\document_loaders\__init__.py:51
49 from langchain_community.document_loaders.bigquery import BigQueryLoader
50 from langchain_community.document_loaders.bilibili import BiliBiliLoader
---> 51 from langchain_community.document_loaders.blackboard import BlackboardLoader
52 from langchain_community.document_loaders.blob_loaders import (
53 Blob,
54 BlobLoader,
55 FileSystemBlobLoader,
56 YoutubeAudioLoader,
57 )
58 from langchain_community.document_loaders.blockchain import BlockchainDocumentLoader
File D:\miniconda\lib\site-packages\langchain_community\document_loaders\blackboard.py:10
7 from langchain_core.documents import Document
9 from langchain_community.document_loaders.directory import DirectoryLoader
---> 10 from langchain_community.document_loaders.pdf import PyPDFLoader
11 from langchain_community.document_loaders.web_base import WebBaseLoader
14 class BlackboardLoader(WebBaseLoader):
File D:\miniconda\lib\site-packages\langchain_community\document_loaders\pdf.py:18
16 from langchain_community.document_loaders.base import BaseLoader
17 from langchain_community.document_loaders.blob_loaders import Blob
---> 18 from langchain_community.document_loaders.parsers.pdf import (
19 AmazonTextractPDFParser,
20 DocumentIntelligenceParser,
21 PDFMinerParser,
22 PDFPlumberParser,
23 PyMuPDFParser,
24 PyPDFium2Parser,
25 PyPDFParser,
26 )
27 from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
29 logger = logging.getLogger(__file__)
File D:\miniconda\lib\site-packages\langchain_community\document_loaders\parsers\__init__.py:5
3 from langchain_community.document_loaders.parsers.grobid import GrobidParser
4 from langchain_community.document_loaders.parsers.html import BS4HTMLParser
----> 5 from langchain_community.document_loaders.parsers.language import LanguageParser
6 from langchain_community.document_loaders.parsers.pdf import (
7 PDFMinerParser,
8 PDFPlumberParser,
(...)
11 PyPDFParser,
12 )
14 __all__ = [
15 "BS4HTMLParser",
16 "DocAIParser",
(...)
24 "PyPDFParser",
25 ]
File D:\miniconda\lib\site-packages\langchain_community\document_loaders\parsers\language\__init__.py:1
----> 1 from langchain_community.document_loaders.parsers.language.language_parser import (
2 LanguageParser,
3 )
5 __all__ = ["LanguageParser"]
File D:\miniconda\lib\site-packages\langchain_community\document_loaders\parsers\language\language_parser.py:24
18 try:
19 from langchain.text_splitter import Language
21 LANGUAGE_EXTENSIONS: Dict[str, str] = {
22 "py": Language.PYTHON,
23 "js": Language.JS,
---> 24 "cobol": Language.COBOL,
25 }
27 LANGUAGE_SEGMENTERS: Dict[str, Any] = {
28 Language.PYTHON: PythonSegmenter,
29 Language.JS: JavaScriptSegmenter,
30 Language.COBOL: CobolSegmenter,
31 }
32 except ImportError:
File D:\miniconda\lib\enum.py:437, in EnumMeta.__getattr__(cls, name)
435 return cls._member_map_[name]
436 except KeyError:
--> 437 raise AttributeError(name) from None
AttributeError: COBOL
### Suggestion:
_No response_
|
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I_kwDOIPDwls56mWy5
| 15,191
|
Issue: lcel `with_fallbacks` streaming
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| 2023-12-27T03:40:56
| 2023-12-27T04:53:55
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|
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I'm going to make a chain through lcel and try to process an error through `with_fallbacks` at the end, but unlike before I put `with_fallbacks`, streaming is not possible and all responses go down at once. Can i process streaming using `with_fallbacks`?
### Suggestion:
lcel `with_fallbacks` streaming
|
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I_kwDOIPDwls56mWZZ
| 15,190
|
Issue: openai api key rate limit error handing
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| 2023-12-27T03:37:31
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| null |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
If the rate limit of the api key is exceeded when developing a chain through lcel, I want to dynamically change another api key and retry to give the client a normal response, is there a way?
### Suggestion:
Dynamically catch an error in lcel, change the api key, and try again
|
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PR_kwDOIPDwls5izIYN
| 15,187
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Use tz-aware utc datetimes in tracer
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| 2023-12-27T00:26:25
| 2024-01-02T20:36:41
| 2024-01-02T20:36:41
|
COLLABORATOR
| 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.
-->
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PR_kwDOIPDwls5izDDV
| 15,186
|
[core] prompt changes
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| 2023-12-26T23:27:43
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|
COLLABORATOR
| null |
change it to pass all variables through all the way in invoke
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PR_kwDOIPDwls5izBf2
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Better input and output schemas for chains that start or end with a R…
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| 2023-12-26T23:03:58
| 2023-12-27T00:45:56
| 2023-12-26T23:21:14
|
COLLABORATOR
| null |
…unnableAssign or RunnablePick
<!-- 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.
-->
|
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PR_kwDOIPDwls5iy6fL
| 15,184
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Implement RunnablePassthrough.pick()
|
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|
COLLABORATOR
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<!-- 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.
-->
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PR_kwDOIPDwls5iy6bK
| 15,183
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community: image_url download + image_url.url
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[] | null | 3
| 2023-12-26T21:35:38
| 2024-01-06T15:44:14
| null |
CONTRIBUTOR
| null |
**Description:**
- adding actual image_url support by downloading the image using requests.get
- support for nested urls
**Issue:**
```python
HumanMessage(
content=[
{"type": "text", "text": "Describe the image:"},
{
"type": "image_url",
"image_url": {
"url": "https://images.unsplash.com/photo-1682687220777-2c60708d6889"
},
},
],
)
```
This was not possible because actual urls were not supportet and url objects inside the image_url was not supported.
|
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PR_kwDOIPDwls5iyzaG
| 15,182
|
Fix runnable vistitor for funcs without pos args
|
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[] | null | 1
| 2023-12-26T20:26:15
| 2023-12-26T20:42:25
| 2023-12-26T20:42:24
|
COLLABORATOR
| 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.
-->
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PR_kwDOIPDwls5iyy-z
| 15,181
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[core: minor] fix getters
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PR_kwDOIPDwls5iyvhE
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|
[core] langauge model like
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PR_kwDOIPDwls5iyr38
| 15,179
|
[core] print ascii
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PR_kwDOIPDwls5iyrrs
| 15,178
|
langchain[minor]: Add stuff docs runnable
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I_kwDOIPDwls56lZ8c
| 15,177
|
DOC: How to write my own short scripts within a fork to test some code?
|
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| 2023-12-26T18:42:18
| 2023-12-26T18:49:22
| null |
NONE
| null |
### Issue with current documentation:
I want to contribute to one of the libs and started a fork. Here are the steps I took:
I am trying to add a new feature but first need to experiment with it. I am unsure on how to get started writing some short scripts to use the libs.
1. I went into ```libs/experimental```, ```libs/core```, ```libs/community``` ```libs/langchain``` and ran ```poetry install``` in all of them.
2. I start an environment from ```libs/langchain``` with ```poetry shell```
3. I created a file inside of it, made some short code:
```from langchain.document_loaders import PyPDFLoader
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
# load the document and split it into chunks
loader = PyPDFLoader("./llm_in_a_flash_apple.pdf")
documents = loader.load_and_split()
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# load it into Chroma
db = Chroma.from_documents(documents, embedding_function)
# query it
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
# print results
print(docs[0].page_content)
```
and got the following error:
```
Traceback (most recent call last):
File "/Users/polo/langchain/libs/langchain/test.py", line 9, in <module>
loader = PyPDFLoader("./llm_in_a_flash_apple.pdf")
File "/Users/polo/langchain/libs/community/langchain_community/document_loaders/pdf.py", line 154, in __init__
raise ImportError(
ImportError: pypdf package not found, please install it with `pip install pypdf`
```
This is my first time in a Python project like this. I am unsure how to get started using all the different packages while in a fork of the repository. If anyone can guide me I would love to make a PR on this, it is quite daunting for beginners to get around and start contributing!
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5iyXr2
| 15,176
|
add multitenancy
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I_kwDOIPDwls56kzEG
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|
pgvecto.rs: retriever filter not working
|
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| 2023-12-26T14:35:49
| 2024-01-15T19:42:01
| 2024-01-15T19:42:01
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NONE
| null |
### System Info
OS: Windows
Python: 3.9.10
Langchain version: 0.0.352
openai version: 1.6.1
### Who can help?
@BeautyyuYanli
@baskaryan
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.vectorstores.pgvecto_rs import PGVecto_rs
from langchain.embeddings import AzureOpenAIEmbeddings
from dotenv import dotenv_values
import os
```
```
config = dotenv_values(".env")
# os.environ["TIKTOKEN_CACHE_DIR"] = "./cache/tiktoken/"
embeddings = AzureOpenAIEmbeddings(
max_retries=3,
timeout=60,
api_key=config["api_key"],
model="text-embedding-ada-002",
openai_api_type=config["api_type"],
azure_endpoint=config["api_base"]
)
URL = "postgresql+psycopg://{username}:{password}@{host}:{port}/{db_name}".format(
port=config["db_port"],
host=config["db_host"],
username=config["db_user"],
password=config["db_pass"],
db_name=config["db_name"],
)
db = PGVecto_rs(
embedding=embeddings,
db_url=URL,
dimension=1536, # text-embedding-ada-002
collection_name="test",
)
```
```
docs = ["a text about mathematics", "a text about physics"]
meta = [{"id": "1"}, {"id": "2"}]
db.add_texts(
texts=docs,
metadatas=meta
)
retr = db.as_retriever(
search_kwargs = {
"k": 1,
"filter": {"id": "1"}
}
)
```
```
retr.invoke("physics")
```
>[Document(page_content='a text about physics', metadata={'id': '2'})]
### Expected behavior
The search should only be performed on documents where the `metadata` field contains `{"id": "1"}`.
In this case, adding a filter makes no difference to the retrieval.
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PR_kwDOIPDwls5ix5KF
| 15,172
|
community: correct spelling mistakes of "Suffle" and "reporoducibility"
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|
CONTRIBUTOR
| null |
- **Description:** Correct spelling mistakes of "Suffle" and "reporoducibility" in `DirectoryLoader` class
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
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core, community: propagate context between threads
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| 2023-12-28T22:51:22
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|
CONTRIBUTOR
| null |
While using `chain.batch`, the default implementation uses a `ThreadPoolExecutor` and run the chains in separate threads. An issue with this approach is that that [the token counting callback](https://python.langchain.com/docs/modules/callbacks/token_counting) fails to work as a consequence of the context not being propagated between threads. This PR adds context propagation to the new threads and adds some thread synchronization in the OpenAI callback. With this change, the token counting callback works as intended.
Having the context propagation change would be highly beneficial for those implementing custom callbacks for similar functionalities as well.
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I_kwDOIPDwls56keUF
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Issue: Explain Memory and How it's implemented in my Case.
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| 2023-12-27T05:34:44
| 2023-12-27T05:34:44
|
NONE
| null |
### Issue you'd like to raise.
`def generate_custom_prompt(new_project_qa,query,name,not_uuid):
check = query.lower()
result = new_project_qa(query)
relevant_document = result['source_documents']
context_text="\n\n---\n\n".join([doc.page_content for doc in relevant_document])
# print(context_text,"context_text")
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:
# Create the custom prompt template
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:"""
# Create the PromptTemplate
custom_prompt = ChatPromptTemplate.from_template(custom_prompt_template)
formatted_prompt = custom_prompt.format(context_text=context_text, check=check)
return formatted_prompt
def retreival_qa_chain(chroma_db_path):
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
llm = ChatOpenAI(temperature=0.1)
retriever = vectordb.as_retriever(search_kwargs={"k": 2})
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever,return_source_documents=True)
return qa`
### Suggestion:
_No response_
|
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community: Async Ollama + ChatOllama
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| 2023-12-26T12:25:43
| 2023-12-26T21:43:49
| 2023-12-26T20:08:04
|
CONTRIBUTOR
| null |
**Description:**
Adding async methods to booth OllamaLLM and ChatOllama to enable async streaming and async .on_llm_new_token callbacks.
**Issue:**
ChatOllama is not working in combination with an AsyncCallbackManager because the .on_llm_new_token method is not awaited.
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Issue: Custom Mistral based LLM from API for RetrievalQA chain
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[] | null | 4
| 2023-12-26T11:56:09
| 2023-12-26T13:43:15
| 2023-12-26T13:43:14
|
NONE
| null |
### Issue you'd like to raise.
I would like to build RAG based on Mistral 7B model
The model is already hosted, and I provide llm_url in the custom LLM setup
I am able to make a request and get a response from the URL using the `llm._call` method, however something is wrong with the callbacks in `RetrievalQA.from_chain_type` method
It gives me below error
`'Mistral7B_LLM' object has no attribute 'callbacks'`
Am I missing anything in the below code
```
from pydantic import Extra
import requests
from typing import Any, List,Dict, Callable, Type, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM, BaseLLM
class Mistral7B_LLM(LLM):
def __init__(self):
self.__post_init__()
def __post_init__(self) -> None:
def _import_mistral7B_llm() -> Any:
from svcs.vector.src.controllers.llm.mistral7B_serving import Mistral7B_LLM
return Mistral7B_LLM
def __getattr__() -> Any:
return Mistral7B_LLM()
def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]:
return {
"Mistral7B_LLM": _import_mistral7B_llm,
}
__all__ = ["Mistral7B_LLM"]
class Config:
extra = Extra.forbid
@property
def _llm_type(self) -> str:
return "Mistral7B_LLM"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
max_new_tokens: Optional[int] = 156,
temperature: Optional[float] = 0.7,
**kwargs: Any,
) -> str:
if stop is not None:
raise ValueError("stop kwargs are not permitted.")
payload = {
"inputs": [prompt],
"max_new_tokens": max_new_tokens,
"temperature": temperature,
}
headers = {"Content-Type": "application/json"}
llm_url = 'my url'
response = requests.post(llm_url, json=payload, headers=headers, verify=False)
response.raise_for_status()
# print("API Response:", response.json())
answer = response.json()["outputs"].split("[/INST]")[-1]
return answer
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"llmUrl": self.llm_url}
```
### Suggestion:
_No response_
|
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| 2,056,338,430
|
PR_kwDOIPDwls5ixpG2
| 15,167
|
Oxford comma, consistent with format elsewhere
|
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[] | null | 1
| 2023-12-26T11:50:31
| 2023-12-26T19:07:10
| 2023-12-26T19:07:09
|
CONTRIBUTOR
| null |
This document uses Oxford comma (A, B, and C), in this list the comma was missing before "and".
This PR corrects that.
<!-- 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.
-->
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PR_kwDOIPDwls5ixkmB
| 15,166
|
core: add .images + .message property to BaseMessage
|
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[] | null | 1
| 2023-12-26T11:22:41
| 2024-01-06T15:38:39
| null |
CONTRIBUTOR
| null |
**Description:**
Adding helper methods for improving implementation speed and code clarity.
**Issue:**
I write a lot of boiler plate and my code feels chaotic since the new vision message schemas.
|
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I_kwDOIPDwls56kIFd
| 15,165
|
Issue: Pass additional data through AgentExecutor
|
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[] | null | 2
| 2023-12-26T10:47:02
| 2023-12-26T20:18:29
| null |
NONE
| null |
### Issue you'd like to raise.
Given a tool that generates a dataframe, how can I pass it through the chain?
```
llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])
prompt = ChatPromptTemplate.from_messages(
[
("system", """
You are a helpful assistant for marketing department.
"""),
MessagesPlaceholder(variable_name="history"),
("user", """
Provide the answer to the question with 3 sentences long.
If the response is related to video-on-demand. Please make sure you return the content id to the answers
Question: {input}
"""),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
agent = (
{
"input": lambda x: x["input"],
"dataframe": <<my_dataframe>>,
"agent_scratchpad": lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
),
"history": lambda x: x['history']
}
| prompt
| llm_with_tools
| OpenAIFunctionsAgentOutputParser()
AgentExecutor(agent=agent, tools=tools, verbose=True)
```
Is it possible?
### Suggestion:
_No response_
|
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| 2,056,226,273
|
I_kwDOIPDwls56j4Xh
| 15,164
|
【BUG】ConversationalRetrievalChain.from_llm and pass in chat_history, there is a problem with the callback.
|
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[] | null | 2
| 2023-12-26T09:32:29
| 2024-01-10T03:36:51
| 2024-01-10T03:36:51
|
NONE
| null |
### System Info
python3.10
langchain 0.0.333
### Who can help?
@hwchase17 @agola11 @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [X] Callbacks/Tracing
- [x] Async
### Reproduction
1. I tried to use the asynchronous call chain=ConversationalRetrievalChain.from_llm combined with the local knowledge base to find the answer.
2. When chat_history is not passed in chain.acall({"question": query, "chat_history":[]}), it can correctly return the result of the streaming output.
3. When I pass in chat_history, the returned result is new_question. new_question is a processed question and is not the answer I want.
code:
` db = FAISS.load_local(COMIXGPT_VECTOR, embeddings)
retriever = db.as_retriever(search_type="similarity_score_threshold",
search_kwargs={"score_threshold": score_threshold,
"k": VECTOR_SEARCH_TOP_K})
prompt = PromptTemplate(
input_variables=["chat_history", "context", "question"],
template=prompt_template
)
chain = ConversationalRetrievalChain.from_llm(
llm=model,
chain_type="stuff",
retriever=retriever,
#memory=memory,
return_source_documents=True,
return_generated_question=True,
combine_docs_chain_kwargs={'prompt': prompt},
condense_question_llm=model,
verbose=True
)
task = asyncio.create_task(wrap_done(
chain.acall({"question": query, "chat_history":chat_history}),
callback.done),
)
if stream:
async for token in callback.aiter():
# Use server-sent-events to stream the response
yield json.dumps({"answer": token}, ensure_ascii=False)
yield json.dumps({"docs": source_documents}, ensure_ascii=False)
else:
answer = ""
async for token in callback.aiter():
answer += token
yield json.dumps({"answer": answer,
"docs": source_documents},
ensure_ascii=False)
await task
return StreamingResponse(knowledge_base_chat_iterator(query=query,
top_k=top_k,
history=history,
chat_history=chat_history,
model_name=model_name,
prompt_name=prompt_name),
media_type="text/event-stream")`
### Expected behavior
I don't know why it called LLM twice, and then it returned the updated Question, which was not the Assistant I wanted. The call log is as follows:
log:
> Entering new LLMChain chain...
Prompt after formatting:
Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
Chat History:
Human: hello
Assistant: Hi there! Is there anything I can help you with? Youre welcome, just tell me~
Human: hello hello make friends
Assistant: ok
Follow Up Input: hello!
Standalone question:
> Entering new StuffDocumentsChain chain...
> Entering new LLMChain chain...
Prompt after formatting:
Chat History:
Human: hello
Assistant: Hi there! Is there anything I can help you with? Youre welcome, just tell me~
Human: hello hello make friends
Assistant: ok
Question: How can I make friends?
Helpful Answer:
2023-12-26 17:27:43,269 - _client.py[line:1758] - INFO: HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
> Finished chain.
> Finished chain.
|
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PR_kwDOIPDwls5ixPjE
| 15,163
|
Corrected an grammatical mistake
|
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[] | null | 1
| 2023-12-26T09:25:56
| 2023-12-26T19:06:53
| 2023-12-26T19:06:53
|
CONTRIBUTOR
| null | null |
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I_kwDOIPDwls56jhti
| 15,162
|
Issue: How it can be splitted ?
|
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[] | null | 1
| 2023-12-26T07:41:33
| 2023-12-26T10:37:39
| 2023-12-26T10:37:39
|
NONE
| null |
is it correct using CharacterTextSplitter in Confluence
### Issue you'd like to raise.
confluence_url = config.get("confluence_url", None)
username = config.get("username", None)
api_key = config.get("api_key", None)
space_key = config.get("space_key", None)
documents = []
embedding = OpenAIEmbeddings()
loader = ConfluenceLoader(
url=confluence_url,
username=username,
api_key=api_key
)
for space_key in space_key:
try:
documents.extend(loader.load(space_key=space_key,include_attachments=True,limit=100))
except:
documents=[]
text_splitter = CharacterTextSplitter(chunk_size=6000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
text_splitter = TokenTextSplitter(chunk_size=1000, chunk_overlap=10, encoding_name="cl100k_base")
texts = text_splitter.split_documents(texts)
### Suggestion:
_No response_
|
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I_kwDOIPDwls56jd3n
| 15,161
|
Does ConversationBufferMemory actually save conversation twice?
|
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[] | null | 2
| 2023-12-26T07:21:01
| 2024-01-02T06:47:11
| 2024-01-02T06:47:11
|
NONE
| null |
### System Info
When I set `verbose=True` when creating chains using ConversationBufferMemory as memory and **redirect** the output to a txt/log file, the return messages shows that the ConversationBufferMemory saves same round conversation twice. You can get the example in later part of this issue.
**This problem will not happen if I just print the return messages in terminal instead of redirecting them into files.**
Does ConversationBufferMemory actually save conversation twice? If so, this will waste half of the input tokens to LLMs. How can I set some variables to make it only save once with any round of conversation?
### 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
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(model_name = 'gpt-4-1106-preview', temperature = 0.0)
def_memory = ConversationBufferMemory(memory_key="history", return_messages=True)
def_chain = ConversationChain(
llm = llm,
memory = def_memory,
verbose = True)
def_queries = ['When answering questions below, you should play a role as a vehicle system engineer. Your job is to read the VDR (Vehicle Digital Requirement) form and evaluate the quality of the VDR completion. Make your answer as brief as you can. If you understand what I said, reply only [UNDERSTAND].',
'You can see duplication in memory of this query.']
for def_q in def_queries:
ret = def_chain.run(def_q)
def_memory.save_context({"input": def_q}, {"output": ret})
# pls redirect the output into some .txt or .log file
```
### Expected behavior
### Below is my redirected gh.log file, I bold the duplicate part
[1m> Entering new ConversationChain chain...[0m
Prompt after formatting:
[32;1m[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
[]
Human: When answering questions below, you should play a role as a vehicle system engineer. Your job is to read the VDR (Vehicle Digital Requirement) form and evaluate the quality of the VDR completion. Make your answer as brief as you can. If you understand what I said, reply only [UNDERSTAND].
AI:[0m
[1m> Finished chain.[0m
[1m> Entering new ConversationChain chain...[0m
Prompt after formatting:
[32;1m[1;3mThe following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
**[HumanMessage(content='When answering questions below, you should play a role as a vehicle system engineer. Your job is to read the VDR (Vehicle Digital Requirement) form and evaluate the quality of the VDR completion. Make your answer as brief as you can. If you understand what I said, reply only [UNDERSTAND].'), AIMessage(content='[UNDERSTOOD]'), HumanMessage(content='When answering questions below, you should play a role as a vehicle system engineer. Your job is to read the VDR (Vehicle Digital Requirement) form and evaluate the quality of the VDR completion. Make your answer as brief as you can. If you understand what I said, reply only [UNDERSTAND].'), AIMessage(content='[UNDERSTOOD]')]**
Human: You can see duplication in memory of this query.
AI:[0m
[1m> Finished chain.[0m
|
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| 2,056,099,364
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I_kwDOIPDwls56jZYk
| 15,160
|
Issue: i want to use langchain callbacks to pass a tool parameter to it? what should i do?
|
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open
| false
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[] | null | 1
| 2023-12-26T06:56:58
| 2023-12-26T07:04:49
| null |
NONE
| null |
### Issue you'd like to raise.
i'm using openai function call agent , gpt llm offen gives bad tool parameters, i want to achieve this: pass certain params to all tools through through some path, before every tool get executed, i can check whether the llm produced params is right or directly use the certain params already get
### Suggestion:
_No response_
|
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| 2,056,076,604
|
I_kwDOIPDwls56jT08
| 15,158
|
Issue: When using Qdrant as retriever, how to retrieve the relevant documents with the similarity score?
|
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open
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[] | null | 4
| 2023-12-26T06:24:17
| 2023-12-26T09:04:08
| null |
NONE
| null |
### Issue you'd like to raise.
When using Qdrant as retriever, how to retrieve the relevant documents with the similarity score? For now, I do not see any methods that I can use to retrieve the documents and also return me the similarity score. However, if use the vector store to run similarity search, I have the option to get the documents and corresponding scores. Isn't there a way to achieve the same thing via retriever?
### Suggestion:
_No response_
|
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I_kwDOIPDwls56jF0c
| 15,157
|
DOC: Need some clarification on Neo4j DB QA chain documentation
|
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[] | null | 3
| 2023-12-26T04:36:18
| 2023-12-26T05:02:10
| null |
NONE
| null |
### Issue with current documentation:
I've wondering that in this part of the code in order to define `cypher generation template` of langchain with neo4j graph database from Neo4j DB QA chain Documentation
```python
from langchain.prompts.prompt import PromptTemplate
CYPHER_GENERATION_TEMPLATE = """Task:Generate Cypher statement to query a graph database.
Instructions:
Use only the provided relationship types and properties in the schema.
Do not use any other relationship types or properties that are not provided.
Schema:
{schema}
Note: Do not include any explanations or apologies in your responses.
Do not respond to any questions that might ask anything else than for you to construct a Cypher statement.
Do not include any text except the generated Cypher statement.
Examples: Here are a few examples of generated Cypher statements for particular questions:
# How many people played in Top Gun?
MATCH (m:Movie {{title:"Top Gun"}})<-[:ACTED_IN]-()
RETURN count(*) AS numberOfActors
The question is:
{question}"""
CYPHER_GENERATION_PROMPT = PromptTemplate(
input_variables=["schema", "question"], template=CYPHER_GENERATION_TEMPLATE
)
chain = GraphCypherQAChain.from_llm(
ChatOpenAI(temperature=0),
graph=graph,
verbose=True,
cypher_prompt=CYPHER_GENERATION_PROMPT,
)
```
Just want to ask that what variables that `schema` and `question` in **input_variables** parameters in `PromptTemplate` refers to ?
### Idea or request for content:
Please explain what schema and question refers to, did schema from our connected neo4j database and question is a text we pass into `chain.run("text input")`. Since i'm a little bit confused with documentation itself and need some explanation. Maybe use an example from it to explain will be much understanable
|
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PR_kwDOIPDwls5iwhOp
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Patch: improve ollama 404 api error message, fix #15147
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|
CONTRIBUTOR
| null |
Make this issue more clearly exposed to developers
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PR_kwDOIPDwls5iweR2
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add vikingdb and embedding
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CONTRIBUTOR
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<!-- 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.
-->
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add vikingdb and embedding
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Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
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- **Issue:** the issue # it fixes if applicable,
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- **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.
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PR_kwDOIPDwls5iwJTP
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refactored `BaseChatMemory`, `ChatMessageHistory`, `get_prompt_input_key`
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COLLABORATOR
| null |
Moved `BaseChatMemory`, `ChatMessageHistory`, `get_prompt_input_key` into `langchain_core`
`BaseChatMemory`, `ChatMessageHistory` are base classes. The `get_prompt_input_key` was moved because of dependency.
Also fixed an error in imports in `langchain_experimental/autonomous_agents/autogpt/memory.py`
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PR_kwDOIPDwls5iv5aP
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community: Semanticscholar tool to search 200M+ scientific articles
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CONTRIBUTOR
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- **Description:** Tool now supports querying over 200 million scientific articles, vastly expanding its reach beyond the 2 million articles accessible through Arxiv. This update significantly broadens access to the entire scope of scientific literature.
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| 15,150
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community: Refactor OpenAIWhisperParserLocal
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| 2023-12-25T17:32:56
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|
CONTRIBUTOR
| null |
This PR addresses an issue in OpenAIWhisperParserLocal where requesting CUDA without availability leads to an AttributeError #15143
Changes:
- Refactored Logic for CUDA Availability: The initialization now includes a check for CUDA availability. If CUDA is not available, the code falls back to using the CPU. This ensures seamless operation without manual intervention.
- Parameterizing Batch Size and Chunk Size: The batch_size and chunk_size are now configurable parameters, offering greater flexibility and optimization options based on the specific requirements of the use case.
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feat: semantic scholar agent tools
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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.
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If you're adding a new integration, please include:
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If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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PR_kwDOIPDwls5ivvHB
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docs: Update dependencies installation cell in steam toolkit
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CONTRIBUTOR
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**Description:** `decouple` is not the correct package, it's `python-decouple`, and the notebook cell doesn't compile.
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Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
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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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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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I_kwDOIPDwls56h6EF
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langchain_community.llms.ollama.OllamaEndpointNotFoundError: Ollama call failed with status code 404
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| 2023-12-26T09:56:06
|
NONE
| null |
### System Info
Langchain Version: 0.0.352
Langchain experimental Version: 0.0.47
Python : 3.10
Ubuntu : 22.04
Poetry is being used
**Code: `test.py`**
```python
import json
from langchain.schema import HumanMessage
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chat_models import ChatOllama
chat_model = ChatOllama(model="mistral:instruct")
json_schema = {
"title": "Person",
"description": "Identifying information about a person.",
"type": "object",
"properties": {
"name": {"title": "Name", "description": "The person's name", "type": "string"},
"age": {"title": "Age", "description": "The person's age", "type": "integer"},
"fav_food": {
"title": "Fav Food",
"description": "The person's favorite food",
"type": "string",
},
},
"required": ["name", "age"],
}
messages = [
HumanMessage(
content="Please tell me about a person using the following JSON schema:"
),
HumanMessage(content=json.dumps(json_schema, indent=2)),
HumanMessage(
content="Now, considering the schema, tell me about a person named John who is 35 years old and loves pizza."
),
]
chat_model_response = chat_model(messages)
```
**Error:**
```sh
Traceback (most recent call last):
File "test.py", line 35, in <module>
chat_model_response = chat_model(messages)
File ".venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 636, in __call__
generation = self.generate(
File ".venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 382, in generate
raise e
File ".venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 372, in generate
self._generate_with_cache(
File ".venv/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 528, in _generate_with_cache
return self._generate(
File ".venv/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py", line 209, in _generate
final_chunk = self._chat_stream_with_aggregation(
File ".venv/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py", line 168, in _chat_stream_with_aggregation
for stream_resp in self._create_chat_stream(messages, stop, **kwargs):
File ".venv/lib/python3.10/site-packages/langchain_community/chat_models/ollama.py", line 155, in _create_chat_stream
yield from self._create_stream(
File ".venv/lib/python3.10/site-packages/langchain_community/llms/ollama.py", line 198, in _create_stream
raise OllamaEndpointNotFoundError(
langchain_community.llms.ollama.OllamaEndpointNotFoundError: Ollama call failed with status code 404.
```
checked if ollama is running on port 11434 it is working fine, but still seeing the issue.
@hwchase17 @agola11
Need some help on this.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
run the file `test.py`
### Expected behavior
model should complete the predication without any issue
|
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Feat: support functions call for google-genai
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CONTRIBUTOR
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I_kwDOIPDwls56hrch
| 15,145
|
SystemMessage are not considered while creating AgentExecutor with OPENAI_FUNCTIONS
|
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| 2023-12-25T12:11:14
| 2023-12-25T12:46:38
| null |
NONE
| null |
### System Info
Langchain version 0.352
SystemMessage is ignored when I invoke AgentExecutor.run function. the code looks as below.
```
from typing import Tuple, Dict
from langchain.agents import initialize_agent, AgentType
from langchain.agents.agent import AgentExecutor
from langchain.agents.format_scratchpad.openai_functions import format_to_openai_function_messages
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.memory import ConversationBufferMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.tools.render import format_tool_to_openai_function
from langchain_core.messages import SystemMessage
from elasticsearch_agent.config import cfg
from elasticsearch_agent.tools.index_data_tool import IndexShowDataTool
from elasticsearch_agent.tools.index_details_tool import IndexDetailsTool
from elasticsearch_agent.tools.index_search_tool import create_search_tool
from elasticsearch_agent.tools.list_indices_tool import ListIndicesTool
tools = [
ListIndicesTool(),
IndexShowDataTool(),
IndexDetailsTool(),
create_search_tool(),
]
def elastic_agent_factory() -> AgentExecutor:
system_msg = """
You are a helpful AI ElasticSearch Expert Assistant
**Always you will get the field names of the ElasticSearch index from the Elasticsearch DB as a first step.
You are provided with various tools to help the user to get information from an ElasticSearch index.
you will get the index name from the question. If not provided, show the list of available indices and ask the user to choose it.
You will generate required aggregation queries for any analytical questions asked.
You will use 'aggregations' field in response object for answering analytical queries.
Dont's:
Never assume index names or field names.
"""
agent_kwargs, memory = setup_memory()
agent_kwargs["system_message"] = SystemMessage(content=system_msg)
return initialize_agent(
tools,
cfg.llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=False,
agent_kwargs=agent_kwargs,
memory=memory
)
def setup_memory() -> Tuple[Dict, ConversationBufferMemory]:
"""
Sets up memory for the open ai functions agent.
:return a tuple with the agent keyword pairs and the conversation memory.
"""
agent_kwargs = {
"extra_prompt_messages": [MessagesPlaceholder(variable_name="memory")],
}
memory = ConversationBufferMemory(memory_key="memory", return_messages=True)
return agent_kwargs, memory
if __name__ == "__main__":
agent_executor = elastic_agent_factory()
prompt = agent_executor.agent.prompt
print(prompt)
print(type(agent_executor.agent.prompt))
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
run the given code
### Expected behavior
The chain agent should consider using the system message and extra prompt message provided to it.
|
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Feat: Terminate retries immediately for User location not supported e…
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| null |
CONTRIBUTOR
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@hwchase17 @efriis
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I_kwDOIPDwls56hX56
| 15,143
|
OpenAIWhisperParserLocal fails when specifying cuda device but cuda is not available
|
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| 2023-12-25T09:53:52
| 2023-12-25T09:56:09
| null |
CONTRIBUTOR
| null |
### System Info
langchain==0.0.352
langchain-community==0.0.6
langchain-core==0.1.3
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
from langchain.document_loaders.parsers.audio import OpenAIWhisperParserLocal
whisper = OpenAIWhisperParserLocal(device="cuda")
```
This fails when cuda is requested but is not available and generates the following error:
`AttributeError: 'OpenAIWhisperParserLocal' object has no attribute 'lang_model'`
This is cause by the following logic: https://github.com/langchain-ai/langchain/blob/a2d30428237695f076060dec881bae0258123775/libs/community/langchain_community/document_loaders/parsers/audio.py#L176C18-L176C21
### Expected behavior
Provide a more clear error or fall back to CPU.
|
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@hwchase17
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open
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[] | null | 1
| 2023-12-25T09:10:42
| 2023-12-25T09:15:40
| null |
NONE
| null |
### System Info
wsl
conda 23.7.4 python 3.8.11
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
repo_id = "Qwen/Qwen-1_8B-Chat"
llm = HuggingFaceHub(
repo_id=repo_id, model_kwargs={"max_length": 128, "temperature": 0.5}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question))
```
output:
```
ValueError: Error raised by inference API: The repository for Qwen/Qwen-1_8B-Chat contains custom code which must be executed to correctly load the model. You can inspect the repository content at https://hf.co/Qwen/Qwen-1_8B-Chat.
Please pass the argument `trust_remote_code=True` to allow custom code to be run.
```
similar issue #6080
|
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PR_kwDOIPDwls5iuwTW
| 15,140
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docs: updated wrong output in `Upstash Redis Cache` section of LLM Ca…
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| 2023-12-25T07:24:34
| 2023-12-26T21:08:22
| 2023-12-26T21:08:21
|
CONTRIBUTOR
| null |
…ching documentation
<!-- 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.
-->
- **Description:** Fixed the wrong output and code block comment in `Upstash Redis` Cache section of LLM Caching documentation,
- **Issue:** #15139 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
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I_kwDOIPDwls56g8_z
| 15,139
|
DOC: Wrong output in `Upstash Redis Cache` section of LLM Caching documentation
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[] | null | 1
| 2023-12-25T07:13:29
| 2023-12-25T07:21:59
| null |
CONTRIBUTOR
| null |
### Issue with current documentation:
In the current documentations the output of `Upstash Redis Cache` section in LLM Caching documentation seems wrong. The second run after caching is done has wrong output and wrong code and comments written in the code block.
### Idea or request for content:
Update the code block with appropriate comment and matching output to remove the confusion.
|
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I_kwDOIPDwls56g1UQ
| 15,138
|
Sagemaker Endpoint not working streaming
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[] | null | 1
| 2023-12-25T06:28:01
| 2023-12-25T06:36:07
| null |
NONE
| null |
### System Info
LangChain : 0.0.352
Python : 3.11.5
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. use streaming
```
llm_sm_ep = SagemakerEndpoint(
endpoint_name=endpoint_name,
client=client,
content_handler=content_handler,
model_kwargs=model_param,
endpoint_kwargs=endpoint_param,
streaming=True,
)
```
### Expected behavior
When I used TGI model, `invoke_endpoint_with_response_stream` of response does't have `outputs`. Instead it return `token` like below full response.
```
data:{"token":{"id":601,"text":" time","logprob":-0.10015869,"special":false},"generated_text":null,"details":null}
```
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fix: add stronger contrast or labeling between notebook input and out…
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| 2023-12-25T06:25:50
| 2023-12-25T06:31:48
| null |
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.
-->
- **Description:** Enhanced the contrast or add labels to distinguish between input and output blocks in the LangChain documentation by modifying the CSS styles for the code blocks in the `custom.css` file referenced in the `docusaurus.config.js` file,
- **Issue:** #14532 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
|
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community: update QuipLoader to support return text instead of html format
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| 2024-01-13T13:46:52
| null |
CONTRIBUTOR
| null |
**Description:**
By default, the quip API return doc as html format, this PR is to support parse html as text to stripe html format.
|
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PR_kwDOIPDwls5iuPS_
| 15,134
|
community: support UAE sentence embeddings
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[] | null | 5
| 2023-12-25T02:36:27
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NONE
| null |
- **Description:** support loading the current SOTA sentence embeddings [WhereIsAI/UAE](https://huggingface.co/WhereIsAI/UAE-Large-V1) in langchain.
- **Dependencies:** [angle_emb](https://github.com/SeanLee97/AnglE)
- **Twitter handle:** @xmlee97
|
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I_kwDOIPDwls56gBu5
| 15,133
|
RetryWithErrorOutputParser does not work with LLMChain because it does not implement the `parse` function
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| 2023-12-24T21:26:43
| 2023-12-25T12:59:48
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NONE
| null |
### Feature request
Currently if one wants to use the RetryWithErrorOutputParser - we need to do the parsing manually instead of generating a chain that does it for us (including all the nice chain functions: batch, ainvoke, etc)
There are 2 issues:
1. The RetryWithOutputParser requires the prompt to be given to it as input so that it can do it's magic. It does this by implementing a `parse_with_prompt` function. Unfortunately this function is not plumbed all the way into the `BaseParser` so that when this is invoked as part of a regular chain it gives the `NotImplementedError: This OutputParser can only be called by the parse_with_prompt method.` exception.
2. The default output of the chatmodels is to return just the output or `AIMessages`. However in this case we need both the prompt and the output.
### Motivation
Currently we need to run the output parsing for the retry parsing manually. This tends to look something like this:
```
chain = chat_prompt | self.chat_model
output_batch = chain.batch(messages_batch,
config={"max_concurrency": 10,
"callbacks": [tracing_callback_handler]})
prompts_list = tracing_callback_handler.prompts
result_list = tracing_callback_handler.results
parsed_output_batch = []
for idx, output in enumerate(output_batch):
parsed_output = retry_parser.parse_with_prompt(output.content, prompts_list[idx])
parsed_output_batch.append(parsed_output)
```
In the above code the `tracing_callback_handler` is a custom callback handler that persists the prompt and results - which we end up using to give the retry_parser the prompt.
This is cumbersome and it would be awesome if this would just work with the chain itself like so
```
chain = chat_prompt | self.chat_model | retry_parser
output_batch = chain.batch(messages_batch,
config={"max_concurrency": 10,
"callbacks": [tracing_callback_handler]})
```
### Your contribution
If someone can validate that my understanding of the problem is correct - I can go ahead and create a PR for this.
|
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I_kwDOIPDwls56gBuA
| 15,132
|
Adding Prompt template to ConversationalRetrievalChain.from_llm
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| 2023-12-24T21:26:16
| 2023-12-24T21:32:55
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NONE
| null |
# How Adding a prompt template to conversational retrieval chain giving the code:
`template= """Use the following pieces of context to answer the question at the end.
If you don't know the answer,
just say that you don't know.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate.from_template(template)
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
from langchain.chains import ConversationalRetrievalChain
qa = ConversationalRetrievalChain.from_llm(
llm,
retriever=db.as_retriever(),
memory=memory,
chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
)
`
`
ValidationError: 1 validation error for ConversationalRetrievalChain
chain_type_kwargs
extra fields not permitted (type=value_error.extra)`
How do I add the prompt template to the chain efficiently?
### Suggestion:
How do I add the prompt template to the chain efficiently? Please, I need help with this.
|
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I_kwDOIPDwls56f24z
| 15,131
|
chain.invoke is no longer taking a json as input
|
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[] | null | 1
| 2023-12-24T17:35:05
| 2023-12-24T17:40:13
| null |
CONTRIBUTOR
| null |
### System Info
windows
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
The below code fails
import os
from operator import itemgetter
from dotenv import load_dotenv
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import DirectoryLoader
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores.faiss import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
load_dotenv()
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
loader = DirectoryLoader("/Users/joyeed/langchain_examples/langchain_examples/data/", glob='**/*.md')
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
text = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY)
vectorstore = FAISS.from_documents(text, embeddings)
retriever = vectorstore.as_retriever()
prompt_template = ChatPromptTemplate.from_template(
"""
Write 2 {platform} posts about {topic}?
"""
)
model = ChatOpenAI(openai_api_key=OPENAI_API_KEY)
# Compose the chain for generating posts
chain = (
{"topic": RunnablePassthrough(), "platform": RunnablePassthrough(), "context": retriever}
| prompt_template
| model
| StrOutputParser()
)
# Invoke the chain to generate a post
output = chain.invoke({"topic": "baseball", "platform": "twitter"})
# Print the generated post
print(output)
I think it is failing because the invoke is expecting a string as an input, but earlier we were able to pass key/value pairs. It is failing in tiktoken/core.py in the below code, it expects a text here.
if match := _special_token_regex(disallowed_special).search(text):
raise_disallowed_special_token(match.group())
### Expected behavior
invoke should allow accepting JSON inputs
|
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Template issue: Neo4J environmental variables in .env file not found
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| null |
### System Info
langchain version: 0.0.352, Windows 10, Python 3.11.6,
### 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
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I'm testing a couple of apps created from Langchain templates and I use dotenv and a .env file in the app's root folder ( default in docs: "my-app" )
I'm not able to work with Neo4J data added into it, when trying to run eg.:
**neo4j-advanced-rag-app\packages\neo4j-advanced-rag\ingest.py**
( My .env is in the neo4j-advanced-rag-app folder )
This is strange, because eg. Langsmith related env vars can be used, so I think the issue is not related to all env vars in .env file!
The last terminal error is:
```
Traceback (most recent call last):
File "d:\Projects\AI_testing\LangChain_test\Python_231026\neo4j-advanced-rag-app\packages\neo4j-advanced-rag\ingest.py", line 16, in <module>
graph = Neo4jGraph()
^^^^^^^^^^^^
File "D:\Projects\AI_testing\LangChain_test\Python_231026\langchain-venv\Lib\site-packages\langchain_community\graphs\neo4j_graph.py", line 65, in __init__
url = get_from_env("url", "NEO4J_URI", url)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Projects\AI_testing\LangChain_test\Python_231026\langchain-venv\Lib\site-packages\langchain_core\utils\env.py", line 41, in get_from_env
raise ValueError(
ValueError: Did not find url, please add an environment variable `NEO4J_URI` which contains it, or pass `url` as a named parameter.
```
### Expected behavior
I expect the app created from a template can use all the env vars in the .env file, which is placed into app root folder.
|
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I_kwDOIPDwls56fjtN
| 15,125
|
Issue: LLMChain error. response_format json error with messages. Messages is array of array
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NONE
| null |
### Issue you'd like to raise.
Environment
```
Edition Windows 11 Home
Version 22H2
Installed on 4/30/2023
OS build 22621.2861
Experience Windows Feature Experience Pack 1000.22681.1000.0
langchain package version: "0.0.212"
zod package version: "3.22.4"
typescript package version: "5.1.6"
```
Prompt
```
My prompt data with keys: {chat_history}, {currentPoint}, {language}, {topic} and Last AI message: {lastAiMessage} and User response: {message}| format: json
```
Creating model code
```
// LLM constructor
constructor(args: any[]) {
this.llm = new ChatOpenAI({
openAIApiKey: process.env.OPENAI_API_KEY,
modelName: 'gpt-3.5-turbo-1106',
modelKwargs: {
response_format: {
type: 'json_object',
},
},
});
this.answerScheme = LLMChain.getAnswerScheme();
this.formatInstructions = createParserFromSchema(this.answerScheme).getFormatInstructions();
const prompt = ChatPromptTemplate.fromMessages([new SystemMessage(args.prompt, { "json": true })]);
const memory = new ConversationSummaryMemory({
llm: this.llm,
memoryKey: 'chat_history',
inputKey: 'message',
});
this.chain = new ConversationChain({
llm: this.llm,
prompt,
memory,
verbose: true,
})
}
private static getAnswerScheme() {
return z.object({
answer: z.string(),
action: z.enum(['none', 'next']),
});
}
```
Send message code
```
async sendMessage(chainValues: ChainValues) {
chainValues['currentPoint'] = this.currentPoint;
chainValues['lastAiMessage'] = this.lastAiMessage ?? '';
try {
const modelKwargs = {
response_format: {
type: 'json_object',
},
};
const rawResponse = await this.chain.call({ ...chainValues, ...modelKwargs, format_instructions: this.formatInstructions });
const { answer, action } = this.answerScheme.parse(rawResponse);
this.lastAiMessage = answer;
this.__parseActionKeyword(action);
return answer;
}
catch (error) {
console.error('LLMChain ERROR:', error);
return "Something goes wrong.\n\n" + error;
}
}
```
LLM run with input
```
[llm/start] [1:chain:ConversationChain > 2:llm:ChatOpenAI] Entering LLM run with input: {
"messages": [
[
{
"lc": 1,
"type": "constructor",
"id": [
"langchain_core",
"messages",
"SystemMessage"
],
"kwargs": {
"content": "My prompt data with keys: {chat_history}, {currentPoint}, {language}, {topic} and Last AI message: {lastAiMessage} and User response: {message}| format: json",
"additional_kwargs": {
"json": true
}
}
}
]
]
}
```
Error: `400 'messages' must contain the word 'json' in some form, to use 'response_format' of type 'json_object'.`
### Suggestion:
_No response_
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PR_kwDOIPDwls5itag7
| 15,124
|
Langchain: Fix typo in documentation
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CONTRIBUTOR
| null |
**Description:**
Fix typo in documentation.
**Twitter handle:**
@HydrogenHydride
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If my agent tool requires user to pass 2 parameters, and if these 2 parameters are not included in the user's question, how can I remind him to enter the parametersIssue: <Please write a comprehensive title after the 'Issue: ' prefix>
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NONE
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### Issue you'd like to raise.
If my agent tool requires user to pass 2 parameters, and if these 2 parameters are not included in the user's question, how can I remind him to enter the parameters
### Suggestion:
_No response_
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Refactor: use SecretStr for Petals llms
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Refactor: use SecretStr for Predibase llms
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Refactor: use SecretStr for StochasticAI llms
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PR_kwDOIPDwls5itSNN
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Refactor: use SecretStr for VolcEngineMaas llms
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I_kwDOIPDwls56fNpi
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Issue: what is RAG and how it's implemented?
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| 2023-12-24T06:39:33
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| null |
NONE
| null |
### Issue you'd like to raise.
what is RAG and how it's implemented as of now I completed exploring custom_prompt_template and want to know more about RAG?
### Suggestion:
_No response_
|
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I_kwDOIPDwls56fMdr
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Chroma.from_documents exclude metadata in embedding? [Question]
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| 2023-12-24T06:13:37
| 2023-12-24T07:07:26
| null |
NONE
| null |
### Issue you'd like to raise.
I suspect a potential issue where Chroma.from_documents might not be embedding and storing vectors for metadata in documents.
I have loaded five tabular documents using DataFrameLoader. However, when attempting to retrieve content based on similarity from the vector store, it appears that sentences in the metadata are not being utilized for matching. I don't see the documentation have clarify if this is the expected behavior or if I might be overlooking a specific argument or setting?
To illustrate, suppose I have a table with three fields: customer_question, agent_answer, and manager_note. If I query using the exact string from one of a manager_note, it surprisingly doesn't return the corresponding document at the top of the results.
**Is this a normal outcome?
Should I modify my table structure to include all relevant content in the page_content_column when setting up the DataFrameLoader?**
Here is the process
```
loader = DataFrameLoader(customer_q_a_001, page_content_column='customer_question')
docs = loader2.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=15)
docs = text_splitter.split_documents(docs )
# db = Chroma.from_documents(all_docs, embeddings, persist_directory="./chroma_db")
# db.persist()
db.similarity_search_with_score((query))
```
Library version:
langchain: 0.0.352
I would appreciate any insights or suggestions regarding this question.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5itNKj
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ci: upgrade actions
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CONTRIBUTOR
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This PR upgrades CI actions [actions/setup-python](https://github.com/actions/setup-python/releases/tag/v5.0.0) and [google-github-actions/auth](https://github.com/google-github-actions/auth/releases/tag/v2.0.0)
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refactored `text_splitter`
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open
| false
| null |
[] | null | 4
| 2023-12-24T01:27:14
| 2024-01-17T19:02:31
| null |
COLLABORATOR
| null |
All text splitters are in a single file. It is hard to extract a base classes to `langchain_core`.
- split code to several files
- added interface classes `BaseTextSplitter`, `BaseTextToDocumentsSplitter` and moved them into `core`
|
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