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https://api.github.com/repos/langchain-ai/langchain/issues/15674 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15674/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15674/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15674/events | https://github.com/langchain-ai/langchain/issues/15674 | 2,069,592,782 | I_kwDOIPDwls57W3rO | 15,674 | class `AzureChatOpenAI` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use langchain_openai.AzureChatOpenAI instead. | {
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] | open | false | null | [] | null | 2 | 2024-01-08T03:59:37 | 2024-01-09T18:10:24 | null | NONE | null | ### Issue with current documentation:
I created an app using AzureOpenAI, and initially, the import statement worked fine:
```
from langchain.chat_models import AzureChatOpenAI
```
My original version details were:
```
langchain==0.0.352
langchain-community==0.0.6
langchain-core==0.1.3
openai==1.6.1
```
Later, I upgraded to:
```
langchain==0.0.354
langchain-community==0.0.9
langchain-core==0.1.7
langchain-experimental==0.0.47
langchain-openai==0.0.2
openai==1.6.1
```
The upgrade led to a deprecation warning for `AzureChatOpenAI`. The suggestion was to use `langchain_openai.AzureChatOpenAI`, but trying to import it gave a `ModuleNotFoundError`. After some trial and error, I found that installing `langchain_openai` separately fixed the issue. Now, I can import `AzureOpenAI`, `AzureOpenAIEmbeddings`, and `AzureChatOpenAI`.
### Idea or request for content:
Despite my research, I couldn't find documentation mentioning the need to install `langchain_openai` separately, which wasted a lot of time and created unnecessary confusion. Sharing this issue here, hope it helps others facing a similar problem. Please add this to the documentation | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15673 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15673/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15673/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15673/events | https://github.com/langchain-ai/langchain/issues/15673 | 2,069,572,435 | I_kwDOIPDwls57WytT | 15,673 | Support for ConversationalRetrievalChain with Azure Cognitive Search as retriever and Azure Open AI as LLM for Streaming Output | {
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] | open | false | null | [] | null | 2 | 2024-01-08T03:42:19 | 2024-01-08T03:56:14 | null | NONE | null | ### Feature request
Hi, I am trying to use ConversationalRetrievalChain with Azure Cognitive Search as retriever with streaming capabilities enabled. The code is not providing the output in a streaming manner. I would like to know if there is any such feature which is supported using Langchain combining Azure Cognitive Search with LLM.
The code snippet I used is as below.
# Code Snippet
def search_docs_chain_with_memory_streaming(
search_index_name=os.getenv("AZURE_COGNITIVE_SEARCH_INDEX_NAME"),
question_list=[],
answer_list=[],
):
code = detect(question)
language_name = map_language_code_to_name(code)
embeddings = OpenAIEmbeddings(
deployment=oaienvs.OPENAI_EMBEDDING_DEPLOYMENT_NAME,
model=oaienvs.OPENAI_EMBEDDING_MODEL_NAME,
openai_api_base=os.environ["OPENAI_API_BASE"],
openai_api_type=os.environ["OPENAI_API_TYPE"],
)
memory = ConversationBufferMemory(memory_key="chat_history", output_key="answer")
acs = AzureSearch(
azure_search_endpoint=os.getenv("AZURE_SEARCH_SERVICE_ENDPOINT"),
azure_search_key=os.getenv("AZURE_COGNITIVE_SEARCH_API_KEY"),
index_name=search_index_name,
search_type="similarity",
semantic_configuration_name="default",
embedding_function=embeddings.embed_query,
)
retriever = acs.as_retriever()
retriever.search_kwargs = {"score_threshold": 0.8} # {'k':1}
print("language_name-----", language_name)
hcp_conv_template = (
get_prompt(workflows, "retrievalchain_hcp_conv_template1", "system_prompt", "v0")
+ language_name +
get_prompt(workflows, "retrievalchain_hcp_conv_template2", "system_prompt", "v0")
)
CONDENSE_QUESTION_PROMPT = get_prompt(workflows, "retrievalchain_condense_question_prompt", "system_prompt", "v0")
prompt = PromptTemplate(
input_variables=["question"], template=CONDENSE_QUESTION_PROMPT
)
SYSTEM_MSG2 = get_prompt(workflows, "retrievalchain_system_msg_template", "system_prompt", "v0")
messages = [
SystemMessagePromptTemplate.from_template(SYSTEM_MSG2),
HumanMessagePromptTemplate.from_template(hcp_conv_template),
]
qa_prompt = ChatPromptTemplate.from_messages(messages)
llm = AzureChatOpenAI(
deployment_name=oaienvs.OPENAI_CHAT_MODEL_DEPLOYMENT_NAME, temperature=0.7, max_retries=4,
#callbacks=[streaming_cb],
streaming=True
#callback_manager=CallbackManager([MyCustomHandler()])
)
qa_chain = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
get_chat_history=lambda o: o,
memory=memory,
condense_question_prompt=prompt,
return_source_documents=True,
verbose=True,
#callback_manager=convo_cb_manager,
#condense_question_llm = llm_condense_ques,
combine_docs_chain_kwargs={"prompt": qa_prompt},
)
if len(question_list) == 0:
question = question + ". Give the answer only in " + language_name + "."
for i in range(len(question_list)):
qa_chain.memory.save_context(
inputs={"question": question_list[i]}, outputs={"answer": answer_list[i]}
)
#return qa_chain.stream({"question": question, "chat_history": []})
return qa_chain
Also I have tried different callback handlers and invoke methods as mentioned in https://gist.github.com/jvelezmagic/03ddf4c452d011aae36b2a0f73d72f68
Kindly suggest if there is any workaround to it.
### Motivation
The motivation is to stream the LLM response using Langchain and Azure Cognitive Search for RAG usecase.
### Your contribution
I have attached the code and the support links in the description. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15672 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15672/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15672/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15672/events | https://github.com/langchain-ai/langchain/issues/15672 | 2,069,449,221 | I_kwDOIPDwls57WUoF | 15,672 | How to manager the new Variables:TypeError: unsupported operand type(s) for |: 'dict' and 'str' | {
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] | open | false | null | [] | null | 5 | 2024-01-08T01:57:00 | 2024-01-08T04:11:17 | null | NONE | null | ### Issue you'd like to raise.
When I deploy the server then it report the error like the above.
How can I fix it ? Thank you !
Heres my code:
`
PROMPT_TEST = """Answer the question based only on the following context:
Based on the previous {history}
Question: {question}
Following: {affection}
Totally,You could select one of the above strategy of mix one.
{format_instructions}
"""`
`chain_with_history_stream = RunnableWithMessageHistory(
{
"question": itemgetter("question"),
"affection": RunnablePassthrough()
}
| PROMPT_TEST | llm,
lambda session_id: MyRedisChatMessageHistory(session_id, url=REDIS_URL),
input_messages_key="question",
history_messages_key="history",
verbose=True,
max_message_history=30,
)`
##Error feedback:
`Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/multiprocessing/spawn.py", line 116, in spawn_main
exitcode = _main(fd, parent_sentinel)
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/multiprocessing/spawn.py", line 125, in _main
prepare(preparation_data)
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/multiprocessing/spawn.py", line 236, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/multiprocessing/spawn.py", line 287, in _fixup_main_from_path
main_content = runpy.run_path(main_path,
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/runpy.py", line 289, in run_path
return _run_module_code(code, init_globals, run_name,
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/runpy.py", line 96, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/root/miniconda3/envs/xdan-chat/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/workspace/xDAN-Dreamy-Chat/app/server.py", line 254, in <module>
{
TypeError: unsupported operand type(s) for |: 'dict' and 'str'`
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15671 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15671/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15671/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15671/events | https://github.com/langchain-ai/langchain/issues/15671 | 2,069,437,208 | I_kwDOIPDwls57WRsY | 15,671 | ImportError: cannot import name '_is_openai_v1' | {
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] | closed | false | null | [] | null | 3 | 2024-01-08T01:46:22 | 2024-01-08T15:49:42 | 2024-01-08T15:49:42 | NONE | null | ### System Info
Hi, I am encountering this error when trying to import anything from the `langchain.embeddings` on Amazon linux AMI with python 3.9 and `langchain==0.0.350`
```python
Traceback (most recent call last):
File "/home/ec2-user/app/search/./app.py", line 9, in <module>
from search import make_chain, postprocess
File "/home/ec2-user/app/search/search.py", line 6, in <module>
from langchain.embeddings import HuggingFaceInstructEmbeddings
File "/home/ec2-user/.local/lib/python3.9/site-packages/langchain/embeddings/__init__.py", line 62, in <module>
from langchain.embeddings.openai import OpenAIEmbeddings
File "/home/ec2-user/.local/lib/python3.9/site-packages/langchain/embeddings/openai.py", line 1, in <module>
from langchain_community.embeddings.openai import (
ImportError: cannot import name '_is_openai_v1' from 'langchain_community.embeddings.openai' (/home/ec2-user/.local/lib/python3.9/site-packages/langchain_community/embeddings/openai.py)
```
The error currently occurs when calling
```python
from langchain.embeddings import HuggingFaceInstructEmbeddings
```
My requirements.txt file looks like this:
```
fastapi==0.105.0
lancedb==0.3.4
langchain==0.0.350
langserve==0.0.36
numpy==1.26.2
pandas==2.1.4
Requests==2.31.0
uvicorn==0.24.0.post1
```
I should note that I've tried reinstalling langchain, openai and transfomers. I've also tried python 3.10 and got the same error.
I should also note that none of my modules are called openai.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] 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
Call `from langchain.embeddings import HuggingFaceInstructEmbeddings` or any of the embeddings modules
### Expected behavior
Should be able to import without errors. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15670 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15670/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15670/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15670/events | https://github.com/langchain-ai/langchain/issues/15670 | 2,069,393,611 | I_kwDOIPDwls57WHDL | 15,670 | multiple ResponseSchema | {
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] | closed | false | null | [] | null | 3 | 2024-01-08T01:02:36 | 2024-01-16T00:48:55 | 2024-01-16T00:48:55 | NONE | null | ### System Info
google-cloud-aiplatform==1.35.0,
langchain-0.0.354
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [X] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
from langchain.prompts import PromptTemplate
from langchain.llms import VertexAI
import vertexai
class bcolors:
SERVER = '\033[92m'
CLIENT = '\033[93m'
ENDC = '\033[0m'
source_response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(
name="source",
description="source used to answer the user's question, should be a website.",
),
]
found_response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(
name="found",
description="whether the model could find the proper answers or not.",
),
]
source_output_parser = StructuredOutputParser.from_response_schemas(source_response_schemas)
found_output_parser = StructuredOutputParser.from_response_schemas(found_response_schemas)
format_instructions = source_output_parser.get_format_instructions()
found_checker = found_output_parser.get_format_instructions()
prompt = PromptTemplate(
template="answer the users question as best as possible.\n{found_cheker}\n{format_instructions}\n{question}",
input_variables=["question"],
partial_variables={"found_checker": found_checker, "format_instructions": format_instructions},
)
vertexai.init(project="my_project_id", location="us-central1")
model = VertexAI(model_name='text-bison@001', max_output_tokens=512, temperature=0.2)
chain = prompt | model | found_output_parser | source_output_parser
while 1:
message = input(bcolors.CLIENT + "Ask to the Cooking Assistant --->> " + bcolors.ENDC)
for s in chain.stream({"question": message}):
print(bcolors.SERVER + "<<<<<<< Cooking Assistant >>>>>>", str(s) + bcolors.ENDC)
```
this code returns the error saying,
```
KeyError: "Input to PromptTemplate is missing variable 'found_cheker'. Expected: ['found_cheker', 'question'] Received: ['question']"
```
### Expected behavior
I expect the model responses would be something like,
```
{'answer': 'proper answer', 'found': True, 'source': 'the source found.'}
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15669 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15669/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15669/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15669/events | https://github.com/langchain-ai/langchain/issues/15669 | 2,069,357,139 | I_kwDOIPDwls57V-JT | 15,669 | Adding response_schemas to ChatPromptTemplate.from_messages prompt design | {
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] | closed | false | null | [] | null | 2 | 2024-01-07T23:58:01 | 2024-01-08T00:59:54 | 2024-01-08T00:59:54 | NONE | null | ### System Info
google-cloud-aiplatform==1.35.0,
langchain-0.0.354
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
response_schemas = [
ResponseSchema(name="answer", description="answer to the user's question"),
ResponseSchema(
name="found check",
description="boolean value (True or False) whether the data found from the reference or not.",
),
]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
qa_prompt = PromptTemplate(
input_variables=[
("system", template),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{question}"),
],
partial_variables={"format_instructions": format_instructions},
)
rag_chain = (
RunnablePassthrough.assign(
context=contextualized_question | temp_retriever | format_docs
)
| qa_prompt
| llm
| remove_prefix
)
```
### Expected behavior
I expect that I could use something like ChatPromptTemplate.from_messages and response_schemas at a same time to return specific value with the conversation history based prompting. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15668 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15668/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15668/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15668/events | https://github.com/langchain-ai/langchain/issues/15668 | 2,069,348,971 | I_kwDOIPDwls57V8Jr | 15,668 | SelfQueryRetriever.from_llm raises following issue: ImportError: Cannot import lark, please install it with 'pip install lark'. | {
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"name": "integration: chroma",
"color": "B78AF8",
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] | closed | false | null | [] | null | 6 | 2024-01-07T23:44:54 | 2024-01-08T09:44:32 | 2024-01-08T09:44:31 | NONE | null | ### System Info
Langchain v0.0.354, Python v3.11, Chroma v0.4.22, Lark v1.1.8
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
def load_self_query_retriever(persist_dir: str, docs: list, metadata_field_info: list, document_content_description = "Information about various documents, the date they are up to date with and where they were sourced from."):
llm = ChatOpenAI(temperature=0)
vectorstore = None
try:
vectorstore = Chroma(persist_directory=persist_dir, embedding_function=get_embedding_function())
except:
vectorstore = Chroma.from_documents(docs, get_embedding_function(), persist_directory=persist_dir)
return SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
)
metadata_field_info = [
AttributeInfo(name="source",description="The document this chunk is from.",type="string",),
AttributeInfo(name="origin",description="The origin the document came from. Bancworks is the higher priority.",type="string",),
AttributeInfo(name="date_day",description="The day the document was uploaded.",type="integer",),
AttributeInfo(name="date_uploaded",description="The month year the document is current to.",type="integer",)
]
self_query_retriever = load_self_query_retriever("storage/deploy/chroma-db-self-query", bancworks_docs, metadata_field_info)
```
The following error is thrown:
```python
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[1], line 110
76 return SelfQueryRetriever.from_llm(
77 llm,
78 vectorstore,
79 document_content_description,
80 metadata_field_info,
81 )
83 metadata_field_info = [
84 AttributeInfo(name="source",description="The document this chunk is from.",type="string",),
85 AttributeInfo(name="origin",description="The origin the document came from. Comes from either scraped websites like TheKinection.org, Kinecta.org or database files like Bancworks. Bancworks is the higher priority.",type="string",),
(...)
107 # ),
108 ]
--> 110 self_query_retriever = load_self_query_retriever("storage/deploy/chroma-db-self-query", bancworks_docs, metadata_field_info)
113 # parent_retriever = load_parent_retriever("full_docs", "storage/deploy/chroma-db-parent")
114
115 # current_place = 0
(...)
127 # retriever.add_documents(bancworks_docs)
128 # retriever.add_documents(bancworks_docs)
Cell In[1], line 76, in load_self_query_retriever(persist_dir, docs, metadata_field_info, document_content_description)
73 llm = ChatOpenAI(temperature=0)
74 vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings(), persist_directory=persist_dir)
---> 76 return SelfQueryRetriever.from_llm(
77 llm,
78 vectorstore,
79 document_content_description,
80 metadata_field_info,
81 )
File /etc/system/kernel/.venv/lib64/python3.11/site-packages/langchain/retrievers/self_query/base.py:225, in SelfQueryRetriever.from_llm(cls, llm, vectorstore, document_contents, metadata_field_info, structured_query_translator, chain_kwargs, enable_limit, use_original_query, **kwargs)
218 if (
219 "allowed_operators" not in chain_kwargs
220 and structured_query_translator.allowed_operators is not None
221 ):
222 chain_kwargs[
223 "allowed_operators"
224 ] = structured_query_translator.allowed_operators
--> 225 query_constructor = load_query_constructor_runnable(
226 llm,
227 document_contents,
228 metadata_field_info,
229 enable_limit=enable_limit,
230 **chain_kwargs,
231 )
232 return cls(
233 query_constructor=query_constructor,
234 vectorstore=vectorstore,
(...)
237 **kwargs,
238 )
File /etc/system/kernel/.venv/lib64/python3.11/site-packages/langchain/chains/query_constructor/base.py:357, in load_query_constructor_runnable(llm, document_contents, attribute_info, examples, allowed_comparators, allowed_operators, enable_limit, schema_prompt, fix_invalid, **kwargs)
353 for ainfo in attribute_info:
354 allowed_attributes.append(
355 ainfo.name if isinstance(ainfo, AttributeInfo) else ainfo["name"]
356 )
--> 357 output_parser = StructuredQueryOutputParser.from_components(
358 allowed_comparators=allowed_comparators,
359 allowed_operators=allowed_operators,
360 allowed_attributes=allowed_attributes,
361 fix_invalid=fix_invalid,
362 )
363 return prompt | llm | output_parser
File /etc/system/kernel/.venv/lib64/python3.11/site-packages/langchain/chains/query_constructor/base.py:99, in StructuredQueryOutputParser.from_components(cls, allowed_comparators, allowed_operators, allowed_attributes, fix_invalid)
96 return fixed
98 else:
---> 99 ast_parse = get_parser(
100 allowed_comparators=allowed_comparators,
101 allowed_operators=allowed_operators,
102 allowed_attributes=allowed_attributes,
103 ).parse
104 return cls(ast_parse=ast_parse)
File /etc/system/kernel/.venv/lib64/python3.11/site-packages/langchain/chains/query_constructor/parser.py:174, in get_parser(allowed_comparators, allowed_operators, allowed_attributes)
172 # QueryTransformer is None when Lark cannot be imported.
173 if QueryTransformer is None:
--> 174 raise ImportError(
175 "Cannot import lark, please install it with 'pip install lark'."
176 )
177 transformer = QueryTransformer(
178 allowed_comparators=allowed_comparators,
179 allowed_operators=allowed_operators,
180 allowed_attributes=allowed_attributes,
181 )
182 return Lark(GRAMMAR, parser="lalr", transformer=transformer, start="program")
ImportError: Cannot import lark, please install it with 'pip install lark'.
```
### Expected behavior
Be able to instantiate SelfQueryRetriever.from_llm successfully | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15667 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15667/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15667/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15667/events | https://github.com/langchain-ai/langchain/issues/15667 | 2,069,343,504 | I_kwDOIPDwls57V60Q | 15,667 | found checker for RAG chain | {
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] | open | false | null | [] | null | 1 | 2024-01-07T23:32:22 | 2024-01-07T23:38:28 | null | NONE | null | ### Feature request
It would be helpful if I can make a RAG chain to output whether it could find the answer from the reference or not as a boolean value.
### Motivation
From personal ideation.
### Your contribution
N/A | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15666 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15666/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15666/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15666/events | https://github.com/langchain-ai/langchain/pull/15666 | 2,069,323,369 | PR_kwDOIPDwls5jai35 | 15,666 | docs: update aws_dynamodb integration doc | {
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- Updated the docs for the memory integration module `aws_dynamodb.ipynb`
- **Issue:**
- #15664
- **Dependencies:**
- N/A | {
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After upgrading to langchain 0.1.0, I received depreciation warnings and updated my imports to langchain_community which cleared that error, then received depreciation warnings about __call__ to Invoke:
The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
I switched the invoke method on the call still get some of the same depreciation warnings. Not sure how to fix this or if it's a bug. code:
```python
qachain = RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever(search_kwargs={"k": args.top_matches}))
res = (qachain.invoke({"query": args.question}))
```
How do I fix this?
### Suggestion:
_No response_ | {
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] | open | false | null | [] | null | 14 | 2024-01-07T21:22:46 | 2024-01-17T02:40:26 | null | COLLABORATOR | null | ### Issue you'd like to raise.
Hello LangChain community,
We're always happy to see more folks getting involved in contributing to the LangChain codebase.
This is a good first issue if you want to learn more about how to set up
for development in the LangChain codebase.
## Goal
Your contribution will make it easier for users to use integrations with the newest LangChain syntax
## Context
As you may have noticed, we’ve recently gone to LangChain 0.1. As part of this, we want to update integration pages to be consistent with new methods. These largely include: (a) new methods for invoking integrations and chains (`invoke`, `stream`), (b) new methods for creating chains (LCEL, `create_xyz_..`).
There are a lot of integrations, so we’d love community help! This is a great way to get started contributing to the library as it will make you familiar with best practices and various integrations.
## Set up for development
There are lots of integration notebooks in https://github.com/langchain-ai/langchain/tree/master/docs/docs/integrations. After making changes there, you should run `make format` from the root LangChain directory to run our formatter.
## Shall you accept
Shall you accept this challenge, please claim one (and only one) of the modules from the list
below as one that you will be working on, and respond to this issue.
Once you've made the required code changes, open a PR and link to this issue.
## Acceptance Criteria
- Uses new methods for calling chains (`invoke`, `stream`, etc)
- Uses LCEL where appropriate
- Follows the format outlined below
## Examples
We've gotten started with some examples to show how we imagine these integration pages should look like. The exact format may look different for each type of integration, so make sure to look at the type you are working on:
- LLMs:
- https://python.langchain.com/docs/integrations/llms/cohere
- Chat Models:
- https://python.langchain.com/docs/integrations/chat/cohere
- Vectorstores:
- https://python.langchain.com/docs/integrations/vectorstores/faiss
- Retrievers:
- https://python.langchain.com/docs/integrations/retrievers/tavily
- https://python.langchain.com/docs/integrations/retrievers/ragatouille
- Tools:
- https://python.langchain.com/docs/integrations/tools/tavily_search
- Toolkits:
- https://python.langchain.com/docs/integrations/toolkits/gmail
- Memory:
- https://python.langchain.com/docs/integrations/memory/sql_chat_message_history
## Your contribution
Please sign up by responding to this issue and including the name of the module.
### Suggestion:
_No response_ | {
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] | open | false | null | [] | null | 2 | 2024-01-07T21:18:23 | 2024-01-16T02:40:34 | null | NONE | 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!
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-->
- **Description:**
This PR adds the option to include vectors that don't have page content. Currently, any such vectors will be skipped. This change can be useful when you're only concerned about the similarity score and not the page content. I'm a contributor to [Rebuff](https://github.com/protectai/rebuff), and we are working on adding a standalone Python SDK. When we query Pinecone, we only need the vector scores. Currently with Langchain, if a Rebuff user sets up their Pinecone database without a metadata field named `"text"`, they will not receive any vector scores when querying Pinecone. With this PR, it won't matter the name of the metadata field used for page content.
For what it's worth, langchain-js does not skip vectors when the page content cannot be found: [pinecone.ts](https://github.com/langchain-ai/langchainjs/blob/8154c7829fc930a5b4060e830d06cbdd3e9484c4/libs/langchain-community/src/vectorstores/pinecone.ts#L230-L234).
I didn't update the Pinecone integration tests because I could not get the tests to pass (even before my changes). They were repeatedly failing because Pinecone is eventually consistent and the tests were assuming the changes would be applied immediately.
- **Issue:** N/A
- **Dependencies:** No changes needed
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## Description:
**PackageInstallTool**: A Dynamic Python Package Installer for Runtime Environments
**Overview**: The `PackageInstallTool` is an essential utility for dynamically installing Python packages within a runtime environment. This tool is especially beneficial in scenarios where executing Python code necessitates the use of specific packages that are not already available in the environment.
**Functionality**:
- **Package Installation**: It allows for the installation of single or multiple Python packages. Users can input either a single package name or a list of package names.
- **Flexibility and Error Handling**: It supports both synchronous and asynchronous operations, although the asynchronous functionality is not implemented yet. The tool also includes error handling to provide feedback in case of installation issues.
**Use Case**: This tool is ideal for environments where Python code execution is dynamic, and there's a need to ensure that all necessary packages are available. It's particularly useful in interactive or automated systems where code dependencies might vary.
**How It Works**:
1. **Input**: The tool accepts a package name or a list of package names as input.
2. **Installation Process**: It uses the `subprocess` module to call `pip install`, ensuring that the packages are installed in the current Python environment.
3. **Feedback**: Post-installation, it provides feedback on the success or failure of the installation process.
**Benefits**:
- **Enhanced Efficiency**: Automates the package installation process, saving time and reducing manual effort.
- **Improved Reliability**: Ensures that the necessary Python packages are available when needed, enhancing the reliability of Python code execution. | {
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- **Issue:** https://github.com/langchain-ai/langchain/issues/14534, https://github.com/langchain-ai/langchain/issues/15039, https://github.com/langchain-ai/langchain/issues/15355
- **Dependencies:** azure-search-documents>=11.4.0
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Langchain Version: 0.0.354 (also tried with 0.1.0)
Python version: 3.9.18
yfinance version: 0.2.35
OS: Windows 10
### Who can help?
@hwchase17 , @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just exectuing the bottom of this page (the tool soley): https://python.langchain.com/docs/integrations/tools/yahoo_finance_news
**Returned this error (see):**
from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool
tool = YahooFinanceNewsTool()
res = tool.run("AAPL")
print(res)
updating langchain to newest version didnt change anything for me.
Im also using a poetry installed file with a clean fresh enviroment, same error.
### Expected behavior
To do exactly whats written in the docs and to not drop an error | {
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I want to use chatopenai as a tool, I need to add the agent chat_history or context into the tool, but the tool generally only accepts a string input, so how do I pass in other parameters
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none | {
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When clicking on redis and then when trying to re-direct to github for seeing the implementation that page is not found
from this [integration's page](https://integrations.langchain.com/memory)
Error :
### Idea or request for content:
i would like to know the place where this has been implemented to fix the issue and raise an PR
Happy to help the community | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15650 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15650/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15650/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15650/events | https://github.com/langchain-ai/langchain/issues/15650 | 2,069,007,389 | I_kwDOIPDwls57Uowd | 15,650 | Creating a conversation agent with tools and history for Ollama | {
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] | closed | false | null | [] | null | 2 | 2024-01-07T06:16:24 | 2024-01-07T15:12:27 | 2024-01-07T15:12:13 | NONE | null | I'm trying to create a simple test that can:
- use Ollama as the model
- use the agent with my custom tools to enrich the output
- history to store the conversation history
Based on examples, the code should look like this:
```
const llm = new ChatOllama(...);
const tools = [...];
const executor = await initializeAgentExecutorWithOptions(tools, llm, ...);
```
the compiler does not like `llm` parameter because
```
Argument of type 'ChatOllama' is not assignable to parameter of type 'BaseLanguageModelInterface<any, BaseLanguageModelCallOptions>'
```
and this is the same for OpenAI llm as well.
I don't see this `BaseLanguageModelCallOptions` interface being used anywhere in the code. Is this the right way to use it? | {
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Issue: The distinction between OpenAI function agents and OpenAI tools was not adequately emphasised. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15647 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15647/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15647/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15647/events | https://github.com/langchain-ai/langchain/issues/15647 | 2,068,909,302 | I_kwDOIPDwls57UQz2 | 15,647 | Issue: how to resolve this warning "My code has a warning "D:\anaconda3\envs\py311\Lib\site-packages\langchain\__init__.py:34: UserWarning: Importing verbose from langchain root module is no longer supported. Please use langchain.globals.set_verbose() / langchain.globals.get_verbose() instead. warnings.warn("" | {
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I have a warning when I run my langchain code "how to resolve this warning "My code has a warning "D:\anaconda3\envs\py311\Lib\site-packages\langchain\__init__.py:34: UserWarning: Importing verbose from langchain root module is no longer supported. Please use langchain.globals.set_verbose() / langchain.globals.get_verbose() instead. warnings.warn(", how to resolve it?
### Suggestion:
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I had no issues running the langchain code before, but when I moved the callback_handler position, this warning appeared: "D:\anaconda3\envs\py311\Lib\site-packages\langchain\__init__.py:34: UserWarning: Importing verbose from langchain root module is no longer supported. Please use langchain.globals.set_verbose() / langchain.globals.get_verbose() instead. warnings.warn("
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] | closed | false | null | [] | null | 4 | 2024-01-06T22:51:01 | 2024-01-07T00:56:13 | 2024-01-07T00:46:03 | NONE | null | ### System Info
LangChain 0.0.354, Python 3.11
### 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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Embedded 1000 or so documents and did a vector similarity search. Came back with a lot of good results. Did the get_relevant_documents call but had no returns. LLM also did not.
My retriever is:
- ParentDocumentRetriever with a parent_splitter and child_splitter
- Parent splits at 2000 tokens. Child splits at 400.
```python
def load_chroma_db(collection_name: str):
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
vectorstore = None
try:
vectorstore = Chroma(persist_directory="storage/deploy/chroma-db", embedding_function=get_embedding_function())
print("Loaded existing vector store")
except:
print("Creating new vector store")
vectorstore = Chroma(
collection_name=collection_name,
embedding_function=get_embedding_function(),
persist_directory="storage/deploy/chroma-db"
)
store = InMemoryStore()
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter
)
return retriever
retriever = load_chroma_db("full_docs")
retriever.vectorstore.similarity_search_with_score("How do I make a zelle transaction?")
```
This returns
```python
[(Document(page_content='Basic Introduction to Zelle® What is Zelle®? Zelle® is a fast, safe, and easy way for members to send money directly between most bank or credit union accounts in the U.S. These Person- to- Person transactions typically occur within minutes. With just an email address or U.S. mobile phone number, members can send money to friends, family, and people they know and trust, regardless of where they', metadata={'date_day': 27, 'date_month': 4, 'date_year': 2023, 'doc_id': '22bfa535-8bc7-4a97-8270-d84b77ba81b0', 'source': 'storage/Bancworks/Zelle & CST FAQs - Internal Use.pdf.txt'}),
0.24197007715702057),
(Document(page_content='Basic Introduction to Zelle® ......................................... 2 Zelle® Transaction Limits / Tiers ............................. 2 Enrollment / Eligible Accounts ................................ 3 Sending / Receiving Transactions ........................... 4 Disputes / Fraud / Scams ......................................... 5 Customer Service Tool (CST)', metadata={'date_day': 27, 'date_month': 4, 'date_year': 2023, 'doc_id': 'eb27b502-c37c-4462-9d8b-488b53c3aa11', 'source': 'storage/Bancworks/Zelle & CST FAQs - Internal Use.pdf.txt'}),
0.24453413486480713),
(Document(page_content='Step 1: Find Zelle in the main menu of the Kinecta mobile banking app. Step 2: Enroll with a U.S. mobile number or email address and select a checking account. Step 3: Start using Zelle. Talking Points: • Zelle is a fast, safe and easy way to send money directly between almost any checking or savings accounts in the U.S., typically within minutes. • With just an email address or U.S. mobile phone', metadata={'date_day': 28, 'date_month': 4, 'date_year': 2023, 'doc_id': '161170fc-8871-412d-a0e7-47e4b1b3d889', 'source': 'storage/Bancworks/Zelle - MarketGram - 20230502.pdf.txt'}),
0.2447606921195984),
(Document(page_content='• Zelle is a fast, safe and easy way to send money directly between almost any checking or savings accounts in the U.S., typically within minutes. • With just an email address or U.S. mobile phone number, send money to people you trust, regardless of where they bank. • Transactions between enrolled consumers typically occur in minutes and generally do not incur transaction fees. • Send, split or', metadata={'date_day': 28, 'date_month': 4, 'date_year': 2023, 'doc_id': '161170fc-8871-412d-a0e7-47e4b1b3d889', 'source': 'storage/Bancworks/Zelle - MarketGram - 20230502.pdf.txt'}),
0.2502959370613098)]
```
If I do the following call:
```python
retriever.get_relevant_documents("How do I make a zelle transaction?", k=4)
```
I get nothing returned.
```python
[]
```
### Expected behavior
Parent documents should be returned based on the child embeddings found. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15643 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15643/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15643/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15643/events | https://github.com/langchain-ai/langchain/issues/15643 | 2,068,840,009 | I_kwDOIPDwls57T_5J | 15,643 | Broken imports | {
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Using...
langchain==0.0.353
langchain-core==0.1.4
Seems to have broken from yesterday's merges?
```
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
--
2319 | File "/root/.local/lib/python3.9/site-packages/langchain/chains/__init__.py", line 56, in <module>
2320 | from langchain.chains.openai_functions import (
2321 | File "/root/.local/lib/python3.9/site-packages/langchain/chains/openai_functions/__init__.py", line 1, in <module>
2322 | from langchain.chains.openai_functions.base import (
2323 | File "/root/.local/lib/python3.9/site-packages/langchain/chains/openai_functions/base.py", line 32, in <module>
2324 | from langchain.utils.openai_functions import convert_pydantic_to_openai_function
2325 | File "/root/.local/lib/python3.9/site-packages/langchain/utils/openai_functions.py", line 1, in <module>
2326 | from langchain_community.utils.openai_functions import (
2327 | File "/root/.local/lib/python3.9/site-packages/langchain_community/utils/openai_functions.py", line 3, in <module>
2328 | from langchain_core.utils.function_calling import (
2329 | ModuleNotFoundError: No module named 'langchain_core.utils.function_calling'
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
It's triggered, in our case, when we import `StuffDocumentsChain`
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
### Expected behavior
No error! | {
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- **Issue:** Fixed #15581
- **Dependencies:** No
- **Twitter handle:** None | {
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<!-- Thank you for contributing to LangChain!
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- Addition of `_astream` implementation in `ctransformers.py`
- Returns asynchronous iterator
Issue:
- Invoking `astream` without any model specific implementation of `_astream` defaults to 'yielding' `ainvoke` which does not "stream" data, returns string asynchronously.
Verification:
- `astream` verified with running `Mistral-7B-Instruct-v0.1-GGUF` | {
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] | open | false | null | [] | null | 3 | 2024-01-06T19:35:55 | 2024-01-06T22:40:22 | null | NONE | null | ### System Info
Langchain
### Who can help?
LangChain with Gemini Pro
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
question = "content pls?"
stuff_answer = stuff_chain(
{"input_documents": pages[1:], "question": question}, return_only_outputs=True
)
### Expected behavior
ReadTimeout: HTTPConnectionPool(host='localhost', port=36027): Read timed out. (read timeout=60.0) | {
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] | closed | false | null | [] | null | 4 | 2024-01-06T10:53:10 | 2024-01-11T20:34:05 | 2024-01-07T00:46:38 | NONE | null | ### Issue you'd like to raise.
I have the following ChromaDB setup:
```python
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
vectorstore = None
try:
vectorstore = Chroma(persist_directory="storage/deploy/chroma-db", embedding_function=get_embedding_function())
print("Loaded existing vector store")
except:
print("Creating new vector store")
vectorstore = Chroma(
collection_name=collection_name,
embedding_function=get_embedding_function(),
persist_directory="storage/deploy/chroma-db"
)
store = InMemoryStore()
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter
)
return retriever
```
The issue is if I add a bunch of documents to the retriever, the memory eventually can run out and crash the system. Is there a way for this to be done out of RAM instead? Or am I misunderstanding the usage of this.
### Suggestion:
Is there a non-inmemory docstore that can be used in the ParentDocumentRetriever or does it not make sense in the use case. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15632 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15632/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15632/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15632/events | https://github.com/langchain-ai/langchain/issues/15632 | 2,068,529,844 | I_kwDOIPDwls57S0K0 | 15,632 | DOC: langchain with Fireworks ai | {
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] | open | false | null | [] | null | 4 | 2024-01-06T10:45:41 | 2024-01-06T16:13:28 | null | NONE | null | ### Issue with current documentation:
https://python.langchain.com/docs/integrations/chat/fireworks
Hi, I'm new Langchain with Fireworks.
I run this code in document 'ChatFireworks' and got an issue.
Environment : python 3.11, Window10
```Create a simple chain with memory
chain = (
RunnablePassthrough.assign(
history=memory.load_memory_variables | (lambda x: x["history"])
)
| prompt
| llm.bind(stop=["\n\n"])
)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[17], line 3
1 chain = (
2 RunnablePassthrough.assign(
----> 3 history=memory.load_memory_variables | (lambda x: x["history"])
4 )
5 | prompt
6 | llm.bind(stop=["\n\n"])
7 )
TypeError: unsupported operand type(s) for |: 'method' and 'function'```
### Idea or request for content:
TypeError: unsupported operand type(s) for |: 'method' and 'function | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15631 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15631/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15631/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15631/events | https://github.com/langchain-ai/langchain/pull/15631 | 2,068,529,290 | PR_kwDOIPDwls5jYC5n | 15,631 | community: Fixed bug of "system message check" in chat_models/tongyi. | {
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] | closed | false | null | [] | null | 1 | 2024-01-06T10:43:56 | 2024-01-07T16:30:22 | 2024-01-07T16:30:19 | CONTRIBUTOR | null | - **Description:** This PR is to fix a bug of "system message check" in langchain_community/ chat_models/tongyi.py
- **Issue:** In term of current logic, if there's no system message in the chat messages, an error of "System message can only be the first message." will be wrongly raised.
- **Dependencies:** No.
- **Twitter handle:** I don't have a Twitter account. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15630 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15630/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15630/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15630/events | https://github.com/langchain-ai/langchain/pull/15630 | 2,068,470,011 | PR_kwDOIPDwls5jX2Ur | 15,630 | community: Provide more actionable errors in the MathPix PDF loader | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15629 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15629/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15629/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15629/events | https://github.com/langchain-ai/langchain/pull/15629 | 2,068,467,164 | PR_kwDOIPDwls5jX1wP | 15,629 | community: Include PDF ID in MathPix metadata | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15628 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15628/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15628/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15628/events | https://github.com/langchain-ai/langchain/issues/15628 | 2,068,438,255 | I_kwDOIPDwls57Sdzv | 15,628 | 'Unrecognized request argument supplied: functions' error when executing agent | following documentation | {
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] | closed | false | null | [] | null | 2 | 2024-01-06T06:53:43 | 2024-01-06T07:02:47 | 2024-01-06T07:02:47 | NONE | null | ### System Info
`langchain==0.1.0`
`langchain-community==0.0.9`
`langchain-core==0.1.7`
`linux 20.04`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Following the documentation https://python.langchain.com/docs/modules/agents/how_to/custom_agent
### Expected behavior
Should output something similar to this
```
{'input': 'How many letters in the word educa',
'output': 'There are 5 letters in the word "educa".'}
```
Instead got an error when ran `agent_executor.invoke({"input": "How many letters in the word educa"})`
```
NotFoundError: Error code: 404 - {'error': {'message': 'Unrecognized request argument supplied: functions', 'type': 'invalid_request_error', 'param': None, 'code': None}}
``` | {
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Python 3.11, Langchain 0.0.354, ChromaDB v0.4.22
### Who can help?
@agola11
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.text_splitter import MarkdownTextSplitter, RecursiveCharacterTextSplitter
from langchain.document_loaders import DirectoryLoader
from langchain.storage import InMemoryStore
from langchain.retrievers import ParentDocumentRetriever
from langchain.vectorstores import Chroma
import chromadb
def load_chroma_db(collection_name: str):
parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000)
child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
vectorstore = Chroma(
collection_name=collection_name,
embedding_function=get_embedding_function(),
persist_directory="storage/deploy/chroma-db"
)
store = InMemoryStore()
retriever = ParentDocumentRetriever(
vectorstore=vectorstore,
docstore=store,
child_splitter=child_splitter,
parent_splitter=parent_splitter
)
return retriever
retriever = load_chroma_db("full_docs")
retriever.add_documents(bancworks_docs)
```
### Expected behavior
Should be able to load ChromaDB and persist it. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15607 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15607/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15607/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15607/events | https://github.com/langchain-ai/langchain/issues/15607 | 2,068,110,708 | I_kwDOIPDwls57RN10 | 15,607 | `UnstructuredFileLoader` shows `TypeError: expected str, bytes or os.PathLike object, not list` when a list of files is passed in | {
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LangChain version: 0.0.354
LangChain Community version: 0.0.8
Platform: Apple M3 Pro chip on MacOS Sonoma (MacOS 14.2.1)
Python version: 3.11.7
### Who can help?
@baskaryan has the most recent commits on this section of the code, but it was for moving items to the `langchain_community` package. I'm not sure who is the original author
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a list of file paths
2. Attempt to load using `UnstructuredFileLoader`
```python
from langchain_community.document_loaders import UnstructuredFileLoader
files = [
"file_1.txt",
"file_2.txt"
]
loader = UnstructuredFileLoader(file_path=files)
documents = loader.load() # Error occurs on this line
```
Contents of `file_1.txt`
```txt
some stuff
```
Contents of `file_2.txt`
```txt
some more stuff
```
Stack trace
```
Traceback (most recent call last):
File "/Users/joshl/Library/Application Support/JetBrains/PyCharm2023.3/scratches/scratch.py", line 9, in <module>
documents = loader.load() # Error occurs on this line
^^^^^^^^^^^^^
File "/Users/joshl/miniforge3/envs/MechanisticLLM/lib/python3.11/site-packages/langchain_community/document_loaders/unstructured.py", line 87, in load
elements = self._get_elements()
^^^^^^^^^^^^^^^^^^^^
File "/Users/joshl/miniforge3/envs/MechanisticLLM/lib/python3.11/site-packages/langchain_community/document_loaders/unstructured.py", line 173, in _get_elements
return partition(filename=self.file_path, **self.unstructured_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/joshl/miniforge3/envs/MechanisticLLM/lib/python3.11/site-packages/unstructured/partition/auto.py", line 278, in partition
filetype = detect_filetype(
^^^^^^^^^^^^^^^^
File "/Users/joshl/miniforge3/envs/MechanisticLLM/lib/python3.11/site-packages/unstructured/file_utils/filetype.py", line 248, in detect_filetype
_, extension = os.path.splitext(_filename)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen posixpath>", line 118, in splitext
TypeError: expected str, bytes or os.PathLike object, not list
Process finished with exit code 1
```
### Expected behavior
A list of strings should be able to be passed to the `UnstructuredFileLoader` class based on the `__init__` arguments
```python
class UnstructuredFileLoader(UnstructuredBaseLoader):
def __init__(
self,
file_path: Union[str, List[str]],
mode: str = "single",
**unstructured_kwargs: Any,
):
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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:
- **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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https://api.github.com/repos/langchain-ai/langchain/issues/15605 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15605/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15605/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15605/events | https://github.com/langchain-ai/langchain/issues/15605 | 2,068,033,254 | I_kwDOIPDwls57Q67m | 15,605 | Playwright Browser Freezing | {
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] | open | false | null | [] | null | 1 | 2024-01-05T20:59:22 | 2024-01-05T21:06:45 | null | NONE | null | ### System Info
## System Info
**LangChain Version:** 0.0.354
**Platform:** MacOS Sonoma 14.2.1
**Python Version:** 3.11.6
### Who can help?
@hwchase17
@agola11
### Information
- [X] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
**Code sample that utilizes the Playwright Toolkit:** [https://python.langchain.com/docs/integrations/toolkits/playwright](https://python.langchain.com/docs/integrations/toolkits/playwright)
For instance, when you attempt to run:
```Python
await navigate_tool.arun(
{"url": "https://web.archive.org/web/20230428131116/https://www.cnn.com/world"}
)
```
Nothing happens after 20 minutes.
The only way I have been able to get a response was by waiting a few seconds and then turning off my computer's wifi, which would return the expected:
```Bash
'Navigating to https://web.archive.org/web/20230428131116/https://www.cnn.com/world returned status code 200'
```
I am utilizing the browser in an agent, and here is the code:
```Python
import asyncio
from typing import Type, Optional
from langchain.agents import AgentExecutor
from langchain.schema import SystemMessage, HumanMessage, AIMessage
from long-chain.agents.format_scratchpad.openai_functions import format_to_openai_function_messages
from langchain.agents.output_parsers.openai_functions import OpenAIFunctionsAgentOutputParser
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.tools.convert_to_openai import format_tool_to_openai_function
from langchain_community.tools.playwright.utils import create_async_playwright_browser
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_community.agent_toolkits.playwright import PlayWrightBrowserToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools import BaseTool
from auto_sentry.src.chat_models.enums.OpenAI import OpenAI as openai_enums
from auto_sentry.src.chat_models.Memory import Memory
from auto_sentry.src.chat_models.OpenAI import GPT
class AIQuestionInput(BaseModel):
query: str = Field(description="should contain your question for the user including specific aspects or details from the user's input that require further clarification or elaboration")
class AIQuestionTool(BaseTool):
name = "ai_question"
description = "useful for when you need to clairify user requests or when you need to ask them a question"
args_schema: Type[BaseModel] = AIQuestionInput
def _run(
self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None
) -> str:
"""Use the tool."""
return query
llm = GPT(model=openai_enums.GPT_4_1106_PREVIEW)
memory = Memory("Test_Conversation")
memory.clear()
search = [TavilySearchResults(api_wrapper=TavilySearchAPIWrapper(), handle_tool_error=True, verbose=True)]
playwright_async_browser = create_async_playwright_browser(headless=True)
toolkit = PlayWrightBrowserToolkit.from_browser(async_browser=playwright_async_browser)
playwright_tools = toolkit.get_tools()
tools:list = search + playwright_tools
MEMORY_KEY = "chat_history"
prompt = ChatPromptTemplate.from_messages(
[
SystemMessage(
content="You are a helpful assistant."
),
MessagesPlaceholder(variable_name=MEMORY_KEY),
HumanMessage(content="{input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
memory.add(prompt.messages[0])
functions=[format_tool_to_openai_function(tool) for tool in tools]
#"""
llm_with_tools = llm.llm.bind(
functions=functions
)
agent = (
{
"input": lambda x: x["input"],
"agent_scratchpad": lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
),
"chat_history": lambda x: x["chat_history"],
}
| prompt
| llm_with_tools
| OpenAIFunctionsAgentOutputParser()
)
agent_executer = AgentExecutor(
agent=agent, tools=tools, verbose=True, return_intermediate_steps=True
)
query = HumanMessage(content=input("> "))
memory.add(query)
async def run_agent():
print(memory.memory())
response = await agent_executer.ainvoke(
{"input": query, MEMORY_KEY: memory.memory()}
)
return response
response = asyncio.run(run_agent())
agent_messages = format_to_openai_function_messages(response["intermediate_steps"])
staged_messages = []
staged_messages.extend(agent_messages)
staged_messages.append(AIMessage(content=response["output"]))
memory.add(staged_messages)
print(memory.memory())
```
And when it executes a Playwright browser-related command, it just freezes and does nothing.
Here is an example that runs a Playwright browser-related command:
```Bash
> Summarize today's financial news from Google Finance.
[SystemMessage(content='You are a helpful assistant.'), HumanMessage(content="Summarize today's financial news from Google Finance.")]
> Entering new AgentExecutor chain...
Invoking: `navigate_browser` with `{'url': 'https://www.google.com/finance'}`
```
But when it utilizes any other tool, such as Tavily Search, it works successfully:
```Bash
> What is the weather supposed to be like in Miami tomorrow?
[SystemMessage(content='You are a helpful assistant.'), HumanMessage(content='What is the weather supposed to be like in Miami tomorrow?')]
> Entering new AgentExecutor chain...
Invoking: `tavily_search_results_json` with `{'query': 'Miami weather forecast for tomorrow'}`
[{'url': 'https://www.weather.gov/mfl/\xa0', 'content': 'Read More > Privacy Policy Miami - South Florida Weather Forecast Office NWS Forecast Of
fice Miami - South Florida Aviation Weather International Weather RADAR IMAGERY National Miami Radar Key West Radar Across Florida CLIMATE Miami - South Florida11691 SW 17th StreetMiami, FL 33165305-229-4522Comments? Questions? Please Contact Us. Last Map Update: Fri, Jan. 5, 2024 at 3:54:05 pm EST Text Product Selector: CURRENT HAZARDS OutlooksMiami - South Florida. Weather Forecast Office. NWS Forecast Office Miami - South Florida. Weather.gov > Miami ... Fri, Jan. 5, 2024 at 2:20:05 am EST. Watches, Warnings & Advisories. Zoom Out: Gale Warning: Small Craft Advisory: Rip Current Statement: ... National Weather Service Miami - South Florida 11691 SW 17th Street Miami, FL 33165 305 ...'}]The weather forecast for Miami tomorrow can be found on the National Weather Service's website for the Miami - South Florida region. You can visit [this link](https://www.weather.gov/mfl/) for the most up-to-date information on the weather forecast, including any watches, warnings, or advisories that may be in effect.
> Finished chain.
[SystemMessage(content='You are a helpful assistant.'), HumanMessage(content='What is the weather supposed to be like in Miami tomorrow?'), AIMes
sage(content='', additional_kwargs={'function_call': {'arguments': '{"query":"Miami weather forecast for tomorrow"}', 'name': 'tavily_search_results_json'}}), FunctionMessage(content='[{"url": "https://www.weather.gov/mfl/\xa0", "content": "Read More > Privacy Policy Miami - South Florida Weather Forecast Office NWS Forecast Office Miami - South Florida Aviation Weather International Weather RADAR IMAGERY National Miami Radar Key West Radar Across Florida CLIMATE Miami - South Florida11691 SW 17th StreetMiami, FL 33165305-229-4522Comments? Questions? Please Contact Us. Last Map Update: Fri, Jan. 5, 2024 at 3:54:05 pm EST Text Product Selector: CURRENT HAZARDS OutlooksMiami - South Florida. Weather Forecast Office. NWS Forecast Office Miami - South Florida. Weather.gov > Miami ... Fri, Jan. 5, 2024 at 2:20:05 am EST. Watches, Warnings & Advisories. Zoom Out: Gale Warning: Small Craft Advisory: Rip Current Statement: ... National Weather Service Miami - South Florida 11691 SW 17th Street Miami, FL 33165 305 ..."}]', name='tavily_search_results_json'), AIMessage(content="The weather forecast for Miami tomorrow can be found on the National Weather Service's website for the Miami - South Florida region. You can visit [this link](https://www.weather.gov/mfl/) for the most up-to-date information on the weather forecast, including any watches, warnings, or advisories that may be in effect.")]
```
### Expected behavior
The behavior of the Playwright Browser should return results to an input in a relatively prompt manner, but it currently freezes and returns. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15604 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15604/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15604/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15604/events | https://github.com/langchain-ai/langchain/issues/15604 | 2,068,029,209 | I_kwDOIPDwls57Q58Z | 15,604 | Issue with LangChain Misclassifying gpt-3.5-turbo-instruct as Chat Model | {
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] | closed | false | null | [] | null | 3 | 2024-01-05T20:55:57 | 2024-01-06T18:29:34 | 2024-01-06T18:29:34 | NONE | null | ### Discussed in https://github.com/langchain-ai/langchain/discussions/15598
<div type='discussions-op-text'>
<sup>Originally posted by **MahdiJafari1** January 5, 2024</sup>
OpenAI deprecated its `text-davinci-003` completion model. I've updated the model to `gpt-3.5-turbo-instruct`. I am encountering an issue with the LangChain where it incorrectly classifies the `gpt-3.5-turbo-instruct` model as a chat model. This is causing initialization problems in my code.
**Environment:**
```
python = "^3.10"
langchain = "^0.0.130"
```
OS: Ubuntu
**Expected Behavior:**
The expected behavior is that the gpt-3.5-turbo-instruct model should be recognized as a completion model by LangChain and initialized appropriately without warnings or errors.
**Actual Behavior:**
When attempting to initialize the gpt-3.5-turbo-instruct model, I receive warnings suggesting that this model is being misclassified as a chat model. The specific warnings are:
```shell
/home/mahdi/.cache/pypoetry/virtualenvs/backend-bRqVKcMN-py3.11/lib/python3.11/site-packages/langchain/llms/openai.py:169: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
/home/mahdi/.cache/pypoetry/virtualenvs/backend-bRqVKcMN-py3.11/lib/python3.11/site-packages/langchain/llms/openai.py:608: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`
```
**My simplified code:**
```python
from langchain import OpenAI
llm = OpenAI({
"model_name": "gpt-3.5-turbo-instruct",
"temperature": 0.0,
"top_p": 1,
"openai_api_key": "API_KEY",
})
print(llm)
```
Output:
```shell
OpenAIChat[Params: {'model_name': 'gpt-3.5-turbo-instruct', 'temperature': 0.0, 'top_p': 1}
```</div> | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15603 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15603/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15603/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15603/events | https://github.com/langchain-ai/langchain/pull/15603 | 2,068,024,344 | PR_kwDOIPDwls5jWU3S | 15,603 | core[patch], community[patch]: mark context, load as beta | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15601 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15601/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15601/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15601/events | https://github.com/langchain-ai/langchain/issues/15601 | 2,067,974,482 | I_kwDOIPDwls57QslS | 15,601 | Issue with ConversationalRetrievalChain in LangChain - ValueError: Missing Input Keys | {
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] | open | false | null | [] | null | 3 | 2024-01-05T20:09:10 | 2024-01-08T14:03:40 | null | NONE | null | ### System Info
langchain 0.0.354, Windows 10,Python 3.11.5
### Who can help?
@hwchase17 @eyurtsev
### 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
input_data = {
"chat_history": chat_history,
"question": question
}
chain = ConversationalRetrievalChain.from_llm(
llm=ChatOpenAI(model=current_model),
retriever=retriever,
memory=memory,
return_source_documents=True,
get_chat_history=lambda h: h
)
result = chain(input_data)
### Expected behavior
I am writing to seek assistance with an issue I've encountered while working with the ConversationalRetrievalChain in LangChain. I have been developing a Discord bot using LangChain and have run into a consistent error that I'm struggling to resolve.
I am trying to use ConversationalRetrievalChain for a chatbot application. However, I keep encountering a ValueError related to input keys. The error message states: "ValueError: Missing some input keys: {'chat_history', 'question'}". This error occurs when I attempt to pass a dictionary containing 'chat_history' and 'question' as separate keys to the ConversationalRetrievalChain.
I have tried various approaches to format the input data correctly, including combining 'chat_history' and 'question' into a single string and passing them as separate keys. Despite these efforts, the error persists. I have also searched for solutions on platforms like Stack Overflow and GitHub but haven't found a resolution that addresses this specific issue.
Could you please provide guidance on how to correctly structure the input data for ConversationalRetrievalChain, or suggest any alternative approaches to resolve this issue? Any insights or recommendations would be greatly appreciated.
Thank you for your time and assistance. I look forward to your response. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15600 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15600/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15600/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15600/events | https://github.com/langchain-ai/langchain/pull/15600 | 2,067,965,774 | PR_kwDOIPDwls5jWH4P | 15,600 | Fix chain docs redirects | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15599 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15599/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15599/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15599/events | https://github.com/langchain-ai/langchain/pull/15599 | 2,067,929,238 | PR_kwDOIPDwls5jV_6F | 15,599 | Populate streamed_output for all runs handled by atransform_stream_with_config | {
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<!-- Thank you for contributing to LangChain!
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https://api.github.com/repos/langchain-ai/langchain/issues/15597 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15597/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15597/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15597/events | https://github.com/langchain-ai/langchain/issues/15597 | 2,067,913,011 | I_kwDOIPDwls57Qdkz | 15,597 | DOC: Lack of Documentation Versioning on Langchain Website | {
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There is no way to view the old documentation on [the official site](https://python.langchain.com/). This makes it extremely difficult to develop. It seems as though every week there is another feature that is deleted, thus another page being deleted.
How is this acceptable? It is becoming almost unusable in the Enterprise world due to the constant changes and lack of documentation.
### Idea or request for content:
Implement a selector that lets you choose which langchain version you're on so that you can view the documentation for that specific version. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15595 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15595/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15595/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15595/events | https://github.com/langchain-ai/langchain/pull/15595 | 2,067,863,755 | PR_kwDOIPDwls5jVxa3 | 15,595 | Allow system message add to history | {
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## Description
This is a follow-up PR to https://github.com/langchain-ai/langchain/pull/14967 that added support for `AIMessage` and `HumanMessage` to `BaseChatMessageHistory.add_user_message` and `BaseChatMessageHistory.add_ai_message`. This extends that to include a `BaseChatMessageHistory.add_system_message` method with the same functionality. It also clarifies the docstrings of the other two methods.
## Dependencies
None
## Tag Maintainers
@hinthornw
@eyurtsev
@butlfrazp
## Note
I ran `make lint` and `make format` but `make test` results in `make: *** No rule to make target 'test'. Stop.`. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15594 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15594/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15594/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15594/events | https://github.com/langchain-ai/langchain/pull/15594 | 2,067,846,671 | PR_kwDOIPDwls5jVtkt | 15,594 | community: Fixing a performance issue with AzureSearch to perform batch embedding | {
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- **Description:** Azure Cognitive Search vector DB store performs slow embedding as it does not utilize the batch embedding functionality. This PR provide a fix to improve the performance of Azure Search class when adding documents to the vector search,
- **Issue:** #11313 ,
- **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.
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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,
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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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https://api.github.com/repos/langchain-ai/langchain/issues/15593 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15593/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15593/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15593/events | https://github.com/langchain-ai/langchain/issues/15593 | 2,067,842,408 | I_kwDOIPDwls57QMVo | 15,593 | AgentTokenBufferMemory does not remove old messages, leading to the "context_length_exceeded" error from OpenAI. | {
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Hello Langchain team,
I have encountered an error while using `AgentTokenBufferMemory` and `RedisChatMessageHistory`. The problem occurs because the buffer is not removing old messages when new ones are added. This causes an issue with OpenAI as the context window exceeds the token limit. Upon investigation, I found that the issue is in the `save_context()` method of the `AgentTokenBufferMemory` class.
```python
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, Any]) -> None:
"""Save context from this conversation to buffer. Pruned."""
input_str, output_str = self._get_input_output(inputs, outputs)
self.chat_memory.add_user_message(input_str)
steps = format_to_openai_function_messages(outputs[self.intermediate_steps_key])
for msg in steps:
self.chat_memory.add_message(msg)
self.chat_memory.add_ai_message(output_str)
# Prune buffer if it exceeds max token limit
buffer = self.chat_memory.messages
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
if curr_buffer_length > self.max_token_limit:
while curr_buffer_length > self.max_token_limit:
buffer.pop(0)
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
```
In the `save_context()` method, the input and output messages are retrieved and added to the chat memory. The intermediate steps are then converted to OpenAI function messages and added to the chat memory as well. If the maximum token limit is exceeded, the buffer is pruned by removing the oldest messages until the current buffer length is below the limit.
However, the problem arises from the line `buffer.pop(0)`, which removes a message from a local variable rather than removing it from the Redis list. Since the list is not pruned, the agent becomes stuck and fails permanently.
Here is the error I am receiving:
```json
{'message': "This model's maximum context length is 16385 tokens. However, your messages resulted in 16602 tokens (16529 in the messages, 73 in the functions). Please reduce the length of the messages or functions.", 'type': 'invalid_request_error', 'param': 'messages', 'code': 'context_length_exceeded'}
```
Here is the information from LangSmith -> Metadata -> Runtime:
<img width="422" alt="image" src="https://github.com/langchain-ai/langchain/assets/101429097/eb3105f2-fd18-4527-b4b2-f89e2e87bdb7">
### Who can help?
@baskarya
### 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
- [X] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import uuid
from langchain.memory.chat_message_histories import RedisChatMessageHistory
from langchain.agents.openai_functions_agent.agent_token_buffer_memory import AgentTokenBufferMemory
from langchain.schema import SystemMessage
from langchain.agents import OpenAIFunctionsAgent, AgentExecutor
from langchain.prompts import MessagesPlaceholder
prompt: SystemMessage = None # my prompt
chat_message_history = RedisChatMessageHistory(
session_id=uuid.uuid4(),
url="redis://localhost:6379",
key_prefix="my_feature_",
ttl=3600,
)
memory = AgentTokenBufferMemory(
chat_memory=chat_message_history,
memory_key="chat_history",
return_messages=True,
llm=llm,
max_token_limit=10000,
)
prompt = OpenAIFunctionsAgent.create_prompt(
system_message=prompt, extra_prompt_messages=[MessagesPlaceholder(variable_name="chat_history")]
)
agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, memory=memory, verbose=True, return_intermediate_steps=True)
agent_response = agent_executor.invoke({"input": user_message})
```
### Expected behavior
Once the current messages have been saved, if the tokens exceed the maximum token limit, the `AgentTokenBufferMemory` should remove old messages from the Redis list (RPOP) until they are below the maximum token limit. | {
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- **Description:** Add dynamic num_variations to MultiQueryRetriever
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] | open | false | null | [] | null | 3 | 2024-01-05T17:29:34 | 2024-01-05T21:25:31 | null | NONE | null | ### Feature request
It would be really great to enhance the VectorStoreRetriever class, by allowing additional (optional) search kwargs to be passed directly to the invoke method. Right now the input type of the invoke method is str, it would be interesting to be able to receive a custom object with "query" and "filters".
Ideally one would be able to do:
```python
chain = (
vector_store.as_retriever()
| parse_documents_to_str
| llm
)
chain.invoke({'query': some_question, 'filter': filter_expression})
```
### Motivation
This change would enable dynamic querying capabilities, such as metadata filtering, which are not currently possible due to the requirement of defining search_kwargs at the constructor level or altering them in the instantiated object. I know I can work this out using a custom retriever class and override some methods, but metadata filtering is a very powerful option to enhance search, and it would be really helpful for developers to have this built in as a default behaviour.
### Your contribution
I can submit a PR if someone confirms this makes sense | {
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] | open | false | null | [] | null | 1 | 2024-01-05T16:20:44 | 2024-01-15T19:40:43 | null | NONE | null | community:
- **Description:**
- Add new ChatLiteLLMRouter class that allows a client to use a LiteLLM Router as a LangChain chat model.
- Note: The existing ChatLiteLLM integration did not cover the LiteLLM Router class.
- Add tests and Jupyter notebook.
- **Issue:** None
- **Dependencies:** Relies on existing ChatLiteLLM integration
- **Twitter handle:** @bburgin_0 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15587 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15587/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15587/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15587/events | https://github.com/langchain-ai/langchain/issues/15587 | 2,067,440,671 | I_kwDOIPDwls57OqQf | 15,587 | In a Chatbot to chat with SQL using openai and langchain, how to integrate the chatbot to make simple conversations | {
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] | open | false | null | [] | null | 7 | 2024-01-05T14:26:07 | 2024-01-08T09:20:06 | null | NONE | null | ### System Info
python: 3.11
langchain: latest
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
in my chatbot which interact with sql db, if im typing hi its giving me the output as the entity of 1st row and 1st column, instead of answering with nothing or invalid question. and some time the response is generated in ascending order and when re runed the query in descending order, how to validate the answer before its given out?
and how can i make my chatbot user friendly, like when user ask hi, it should greet back or when there is informal question it should just reply
import pandas as pd
import sqlalchemy as sal
import os, sys, openai
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
from langchain.prompts import ChatPromptTemplate
# import ChatOpenAI
from langchain.memory import ConversationBufferMemory
# import sql_cmd
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
# from langchain import PromptTemplate
from langchain.prompts import PromptTemplate
# from langchain.prompts.PromptTemplate import PromptTemplate
# from langchain.models import ChatGPTClient
# from langchain.utils import save_conversation
os.environ['OPENAI_API_KEY'] = openapi_key
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
# custom_suffix = """""
# If the SQLResult is empty, the Answer should be "No results found". DO NOT hallucinate an answer if there is no result."""
engine = create_engine(connection_uri)
def chat(question,sql_format):
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
# db2 = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
# db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
answer = None
if sql_format==False:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
answer = db_chain.run(question)
else:
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True , return_sql =True)
sql_query = db_chain.run(question)
print("SQLQuery: "+str(sql_query))
# result = engine.execute(sql_query)
result_df = pd.read_sql(sql_query, engine)
answer = result_df.to_dict()
from langchain.prompts import PromptTemplate
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the SQLResult as answer.
The question: {db_chain.run}
"""
prompt_template = """" Use the following pieces of context to answer the question at the end.
If you don't know the answer, please think rationally and answer from your own knowledge base.
Don't consider the table which are not mentioned, if no result is matching with the keyword Please return the answer as invalid question
{context}
Question: {questions}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "questions"]
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
return answer
def chain1(question):
text = chat(question,False)
return text
def chain2(question):
query = chat(question,True)
return query
it uses "\Lib\site-packages\langchain_experimental\sql\base.py"
"\Lib\site-packages\langchain_experimental\sql\vector_sql.py"
"\Lib\site-packages\langchain_experimental\sql\prompt.py"
### Expected behavior
in my chatbot which interact with sql db, if im typing hi its giving me the output as the entity of 1st row and 1st column, instead of answering with nothing or invalid question. and some time the response is generated in ascending order and when re runed the query in descending order, how to validate the answer before its given out?
and how can i make my chatbot user friendly, like when user ask hi, it should greet back or when there is informal question it should just reply
| {
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https://api.github.com/repos/langchain-ai/langchain/issues/15586 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15586/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15586/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15586/events | https://github.com/langchain-ai/langchain/issues/15586 | 2,067,418,916 | I_kwDOIPDwls57Ok8k | 15,586 | RAG chain response often includes "\n AI:" in front of actual response | {
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] | closed | false | null | [] | null | 4 | 2024-01-05T14:12:29 | 2024-01-16T00:49:32 | 2024-01-16T00:49:32 | NONE | null | ### System Info
google-cloud-aiplatform==1.35.0,
langchain-0.0.354
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
template = """
**Role:**
You are a helpful assistant.
**Context:**
You have to only use a reference stored document to generate a response.
CONTEXT: {context}
**Task:**
1. task 1
- some important requirements for task 1
2. task 2
- some important requirements for task 2
3. task 3
- some important requirements for task 3
Question: {question}
Helpful Answer:
**Resource:** [reference source name]
"""
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", template),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{question}"),
]
)
rag_chain = (
RunnablePassthrough.assign(
context=contextualized_question | retriever | format_docs
)
| qa_prompt
| llm
)
response = rag_chain.invoke({"question": message, "chat_history": memory.get_history()})
print(response)
```
### Expected behavior
I expect to get the response which is composed of only a proper response sentence. Sometimes, it does well as I expected.
But frequently, it returns "\n AI:" in front of the actual response which is not suppose to do so.
For example,
```
"\n AI: The reference document says blah blah.",
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15585 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15585/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15585/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15585/events | https://github.com/langchain-ai/langchain/issues/15585 | 2,067,361,958 | I_kwDOIPDwls57OXCm | 15,585 | Chroma as_retriever function with kwargs leads to unexpected keyword argument | {
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"name": "integration: chroma",
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"description": "Related to ChromaDB"
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] | open | false | null | [] | null | 5 | 2024-01-05T13:35:08 | 2024-01-07T07:11:28 | null | NONE | null | ### System Info
langchain = 0.0.354
This problem appears since commit 62d32bd
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Comit 62d32bd allowed to pass kwarg to ChromaDb. This is really nice but I my case it leads to an error ...
I don't know if it is relevant or a workaround exists so I raise the issue
Here is how to reproduce
``` python
from langchain.vectorstores import Chroma
# load a simple Chroma DB
vectordb = Chroma(persist_directory=my_chroma_db_directory,
embedding_function=my_embedding)
# init a retriever function
retriever = vectordb.as_retriever(search_kwargs={"k": retriever_output_number, "fetch_k": retriever_documents_used, "lambda_mult": retriever_diversity})
# call the retriever in a qa
qa_chain = RetrievalQA.from_chain_type(llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=False,
verbose=True)
# ask something
answer = qa_chain.run("find something in doc ...")
```
### Expected behavior
kwargs are needed by the `as_retriever` function, in the example above the argument `fetch_k` is mandatory
https://github.com/langchain-ai/langchain/blob/fd5fbb507dd3b1c189aa1e4b601b8669217b0f78/libs/core/langchain_core/vectorstores.py#L553
Since commit 62d32bd kwargs are passed up to Chroma `function similarity_search_with_score` which calls `__query_collection` leading to the error
https://github.com/langchain-ai/langchain/blob/fd5fbb507dd3b1c189aa1e4b601b8669217b0f78/libs/community/langchain_community/vectorstores/chroma.py#L408
TypeError: Collection.query() got an unexpected keyword argument 'fetch_k'
Is there a way to avoid that ? | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15584 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15584/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15584/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15584/events | https://github.com/langchain-ai/langchain/issues/15584 | 2,067,357,036 | I_kwDOIPDwls57OV1s | 15,584 | Clickhouse SQL Database Agent | {
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] | closed | false | null | [] | null | 2 | 2024-01-05T13:31:32 | 2024-01-06T07:38:01 | 2024-01-06T07:38:01 | NONE | null | ### System Info
Python 3.10.11
Mac M1
Langchain Version: 0.0.353
openai Version: 0.28.0
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.agents.agent_types import AgentType
from langchain.llms.openai import OpenAI
from langchain.sql_database import SQLDatabase
db = SQLDatabase.from_uri("clickhouse+http://clickhouse_admin:P%21%40ssword42%21@localhost:8123/idfyDB")
toolkit = SQLDatabaseToolkit(db=db, llm=OpenAI(temperature=0))
agent_executor = create_sql_agent(
llm=OpenAI(model_name='gpt-4', temperature=0),
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
agent_executor.run("Describe table sample_tbl")
### Expected behavior
The SQLDatabase toolkit should be able to query the clickhouse database as I have installed and provided the right dialect and connecting directly via sqlalchemy works fine. The issue is langchain's sql database toolkit somehow is unable to query the clickhouse database, it works fine for the chinook db sqlite file. Logs for reference -
> Entering new AgentExecutor chain...
Action: sql_db_list_tables
Action Input:
Observation:
Thought:I need to check the tables in the database to see if there is an "eve_tasks_executed" table.
Action: sql_db_list_tables
Action Input:
Observation:
Thought:Traceback (most recent call last):
File "/Users/vivekkalyanarangan/opt/anaconda3/envs/streaml/lib/python3.10/site-packages/langchain/agents/agent.py", line 1066, in _iter_next_step
output = self.agent.plan(
File "/Users/vivekkalyanarangan/opt/anaconda3/envs/streaml/lib/python3.10/site-packages/langchain/agents/agent.py", line 636, in plan
return self.output_parser.parse(full_output)
File "/Users/vivekkalyanarangan/opt/anaconda3/envs/streaml/lib/python3.10/site-packages/langchain/agents/mrkl/output_parser.py", line 63, in parse
raise OutputParserException(
langchain_core.exceptions.OutputParserException: Could not parse LLM output: `I don't know the answer to the question because I don't have access to the list of tables in the database.`
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
agent_executor.run("Run select * query on eve_tasks_executed table")
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
raise e
self._call(inputs, run_manager=run_manager)
next_step_output = self._take_next_step(
[
[
raise ValueError(
ValueError: An output parsing error occurred. In order to pass this error back to the agent and have it try again, pass `handle_parsing_errors=True` to the AgentExecutor. This is the error: Could not parse LLM output: `I don't know the answer to the question because I don't have access to the list of tables in the database.` [hidden file names]
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https://api.github.com/repos/langchain-ai/langchain/issues/15583 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15583/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15583/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15583/events | https://github.com/langchain-ai/langchain/issues/15583 | 2,067,325,836 | I_kwDOIPDwls57OOOM | 15,583 | Issue: ipykernel kernel crashes when using llama-2-13b model | {
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] | open | false | null | [] | null | 1 | 2024-01-05T13:08:21 | 2024-01-05T13:21:14 | null | NONE | null | Hi,
I am using langchain and llama-cpp-python to do some QA on a text file. When using the llama-2-13b-chat quantized model from [HuggingFace](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF/blob/main/llama-2-13b-chat.Q5_K_M.gguf). I am able to create a RetrievalQA chain passing the vectorstore and prompt, but when I use the chain.run(query), it crashes the anaconda kernel.
I tried using the [7b variant](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q5_K_M.gguf) and this works fine without any issue.
### Package Version
langchain==0.0.350
langchain-community==0.0.3
langchain-core==0.1.1
langsmith==0.0.70
llama_cpp_python==0.2.19
### Code Snippet
from langchain.text_splitter import RecursiveCharacterTextSplitter,SentenceTransformersTokenTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings, SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import LlamaCpp
from langchain import PromptTemplate
from langchain.chains import RetrievalQA
model_path="Model Path/llama-2-13b-chat.Q5_K_M.gguf"
llm = LlamaCpp(
model_path = model_path,
max_tokens=700,f16_kv=True,model_kwargs={'context_length':4000,'temparature':0.3}
)
embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device':'cpu'})
vectorstore = FAISS.load_local(f'Text File', embeddings=embeddings)
template="""
Answer the question using only the context provided to you.
Context: {context}
Question:{question}
"""
qa_prompt=PromptTemplate(template=template, input_variables=["context","question"])
chain = RetrievalQA.from_chain_type(llm=llm,
chain_type='stuff',
retriever=vectorstore.as_retriever(),
return_source_documents=False,
chain_type_kwargs={'prompt': qa_prompt})
chain.run("Who are the people in the conversation?")
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15582 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15582/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15582/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15582/events | https://github.com/langchain-ai/langchain/pull/15582 | 2,067,223,975 | PR_kwDOIPDwls5jTjki | 15,582 | community: Chat Huggingface and HuggingFace TGI llms | {
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] | open | false | null | [] | null | 3 | 2024-01-05T12:00:33 | 2024-01-17T07:15:43 | null | NONE | null | community:
- **Description:**
- [x] The method `_resolve_model_id()` is only called when `model_id` is not explicitly defined, in order to avoid calling `list_inference_endpoints` when connecting `HuggingFaceTextGenInference` to a self-hosted TGI server.
- [x] For `HuggingFaceTextGenInference` clients that send request to a HuggingFace-deployed Inference Endpoints, a Bearer Token is needed for authorization.
- [x] More integrations tests is added for `HuggingFaceTextGenInference`
- **Issue:** Referring to this current [issue](https://github.com/huggingface/text-generation-inference/issues/747) in HuggingFace TGI repo
- **Dependencies:** No dependencies for this change
- **Twitter handle:** @MinhN2804
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https://api.github.com/repos/langchain-ai/langchain/issues/15581 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15581/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15581/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15581/events | https://github.com/langchain-ai/langchain/issues/15581 | 2,067,198,112 | I_kwDOIPDwls57NvCg | 15,581 | metadata is not properly processed when the field does not exists | {
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] | closed | false | null | [] | null | 1 | 2024-01-05T11:39:32 | 2024-01-07T01:05:01 | 2024-01-07T01:05:01 | NONE | null | ### System Info
In the langchain_community/vectorstores/azuresearch.py on line 656 the filed name is used explicitly, which leads to an error if the index does not have the mentioned filed. The suggestion is to replace
`json.loads(result["metadata").get("key"), ""),`
with `json.loads(result[FIELDS_METADATA]).get("key"), "") if FIELDS_METADATA in result else "",`
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Configure Azure Search index without metadata field.
2. execute the following code `docs = vector_store.semantic_hybrid_search_with_score_and_rerank(
query=query, k=3
)`
### Expected behavior
successful execution of the above mentioned code | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15580 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15580/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15580/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15580/events | https://github.com/langchain-ai/langchain/issues/15580 | 2,067,191,288 | I_kwDOIPDwls57NtX4 | 15,580 | ValueError: variable chat_history should be a list of base messages, got [HumanMessage(content='input message'), "output response"] | {
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] | closed | false | null | [] | null | 1 | 2024-01-05T11:34:20 | 2024-01-05T13:53:05 | 2024-01-05T13:53:05 | NONE | null | ### System Info
google-cloud-aiplatform==1.35.0,
langchain-0.0.354
### 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
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
class ChatbotHistory:
def __init__(self, max_size=5):
self.buffer = deque(maxlen=max_size)
def add_interaction(self, user_message, bot_response):
# Assuming HumanMessage is a class that stores a message content
self.buffer.append(HumanMessage(content=user_message))
self.buffer.append(bot_response)
def get_history(self):
return list(self.buffer)
def get_history_as_string(self):
history_string = ""
for message in self.buffer:
if isinstance(message, HumanMessage):
history_string += f"User: {message.content}\n"
else: # Assuming bot responses are strings
history_string += f"Bot: {message}\n"
return history_string.strip()
def clear_history(self):
self.buffer.clear()
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
def contextualized_question(input: dict):
llm = VertexAI(model_name='text-bison@001', max_output_tokens=512, temperature=0.2)
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is."""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
contextualize_q_chain = contextualize_q_prompt | llm | StrOutputParser()
if input.get("chat_history"):
return contextualize_q_chain
else:
return input["question"]
if not message:
message = request.form.get('userInput')
template = """
CONTEXT: {context}
Question: {question}
Helpful Answer:
**Resource:** [reference source name]
"""
# rag_prompt_custom = PromptTemplate.from_template(template)
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", template),
MessagesPlaceholder(variable_name="chat_history"),
("human", "{question}"),
]
)
rag_chain = (
RunnablePassthrough.assign(
context=contextualized_question | temp_retriever | format_docs
)
| qa_prompt
| llm
)
response = rag_chain.invoke({"question": message, "chat_history": memory.get_history()})
memory.add_interaction(message, response)
```
### Expected behavior
I expected to have string or json format output response as an output of invoke(),
but it showed me an error saying,
```
ValueError: variable chat_history should be a list of base messages, got [HumanMessage(content='input message'), "output response"]
```
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https://api.github.com/repos/langchain-ai/langchain/issues/15579 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15579/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15579/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15579/events | https://github.com/langchain-ai/langchain/issues/15579 | 2,067,188,914 | I_kwDOIPDwls57Nsyy | 15,579 | Cannot import name 'URL' from 'sqlalchemy' | {
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] | open | false | null | [] | null | 3 | 2024-01-05T11:32:28 | 2024-01-07T17:01:13 | null | NONE | null | ### Discussed in https://github.com/langchain-ai/langchain/discussions/5701
<div type='discussions-op-text'>
<sup>Originally posted by **rdhillbb** June 5, 2023</sup>
Newbie here.
I found an issue while importing 'VectorstoreIndexCreator'
ImportError: cannot import name 'URL' from 'sqlalchemy' (/Users/tst/anaconda3/lib/python3.10/site-packages/sqlalchemy/__init__.py)
Error Log:
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
Cell In[88], line 2
1 from langchain.docstore.document import Document
----> 2 from langchain.indexes import VectorstoreIndexCreator
3 from langchain_community.utilities import ApifyWrapper
5 apify = ApifyWrapper()
File ~/anaconda3/lib/python3.10/site-packages/langchain/indexes/__init__.py:17
1 """Code to support various indexing workflows.
2
3 Provides code to:
(...)
14 documents that were derived from parent documents by chunking.)
15 """
16 from langchain.indexes._api import IndexingResult, aindex, index
---> 17 from langchain.indexes._sql_record_manager import SQLRecordManager
18 from langchain.indexes.graph import GraphIndexCreator
19 from langchain.indexes.vectorstore import VectorstoreIndexCreator
File ~/anaconda3/lib/python3.10/site-packages/langchain/indexes/_sql_record_manager.py:21
18 import uuid
19 from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence, Union
---> 21 from sqlalchemy import (
22 URL,
23 Column,
24 Engine,
25 Float,
26 Index,
27 String,
28 UniqueConstraint,
29 and_,
30 create_engine,
31 delete,
32 select,
33 text,
34 )
35 from sqlalchemy.ext.asyncio import (
36 AsyncEngine,
37 AsyncSession,
38 async_sessionmaker,
39 create_async_engine,
40 )
41 from sqlalchemy.ext.declarative import declarative_base
Thank You
Vj | {
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] | closed | false | null | [] | null | 1 | 2024-01-05T10:59:18 | 2024-01-08T11:15:05 | 2024-01-07T16:48:15 | CONTRIBUTOR | null | fix of #14905
<!-- 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,
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If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
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https://api.github.com/repos/langchain-ai/langchain/issues/15577 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15577/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15577/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15577/events | https://github.com/langchain-ai/langchain/issues/15577 | 2,067,106,521 | I_kwDOIPDwls57NYrZ | 15,577 | Issue:How can I resolve memory with conversation retreival chain error? | {
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] | open | false | null | [] | null | 1 | 2024-01-05T10:33:22 | 2024-01-05T10:46:17 | null | NONE | null | ### Issue you'd like to raise.
below is my code
def generate_custom_prompt(query=None,name=None,not_uuid=None,chroma_db_path=None):
check = query.lower()
embedding = OpenAIEmbeddings()
vectordb = Chroma(persist_directory=chroma_db_path, embedding_function=embedding)
retriever = vectordb.as_retriever(search_kwargs={"k": 2})
relevant_document = retriever.get_relevant_documents(query)
print(relevant_document,"*****************************************")
context_text="\n\n---\n\n".join([doc.page_content for doc in relevant_document])
# 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: {{chat_history}}
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 = PromptTemplate(input_variables=["context_text","check","chat_history"],template=custom_prompt_template)
formatted_prompt = custom_prompt.format(context_text=context_text, check=check)
# QA_CHAIN_PROMPT = PromptTemplate(
# input_variables=["context_text","check"],template=custom_prompt_template,)
llm = ChatOpenAI(temperature=0.1)
memory = ConversationBufferMemory(memory_key='chat_history', output_key='answer', return_messages=True)
# qa = ConversationalRetrievalChain.from_llm(llm=llm,memory=memory,chain_type="stuff",retriever=retriever,return_source_documents=True,get_chat_history=lambda h : h,verbose=True)
qa = ConversationalRetrievalChain.from_llm(llm=llm,memory=memory,chain_type="stuff",retriever=retriever,return_source_documents=True,get_chat_history=lambda h : h,verbose=True, combine_docs_chain_kwargs={"prompt": custom_prompt})
# prompt_qa={"qa": qa, "formatted_prompt": formatted_prompt}
return qa
The error I am getting is:
File "/usr/lib/python3.8/string.py", line 272, in get_field
obj = self.get_value(first, args, kwargs)
File "/usr/lib/python3.8/string.py", line 229, in get_value
return kwargs[key]
KeyError: 'chat_history'
_No response_
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https://api.github.com/repos/langchain-ai/langchain/issues/15576 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15576/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15576/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15576/events | https://github.com/langchain-ai/langchain/issues/15576 | 2,067,029,408 | I_kwDOIPDwls57NF2g | 15,576 | Issue: Pypdf extract_image=True is not working on docker(production) | {
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] | open | false | null | [] | null | 1 | 2024-01-05T09:42:50 | 2024-01-05T09:52:03 | null | NONE | null | ### Issue you'd like to raise.
Hello Team,
we are trying to use pypdf to get the text out from the pdf use the chunk for embedding(details are there in attached code snippet. while using, i have installed all the required packages. its working fine in my local(windows 10). same code snippet and requirement.txt if i use on docker that uses ubuntu OS(its in prod), i am getting below error:
**Error while chunking the file: Error while chunking the file, Errored while loading the document: `rapidocr-onnxruntime` package not found, please install it with `pip install rapidocr-onnxruntime**`
The strage part here is this **rapidocr-onnxruntime** package is already installed on the ubuntu system**(i re-verified by seeing the github action runner logs where it installs all the packages from requirement.txt)**
Not able to understand why on prod it **pypdf package with extract_image=True** is throwing above error
It will be helpful, if you can provide any insight or workaround to this issue.
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The current implementation does not exploit the cache when using stream.
This PR add the usage of the llm cache with the stream.
**Twitter handle:**
@pprados
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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:
- **Description:** a description of the change,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
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- **Description:** Update pgvecto_rs to use its high level sdk,
- **Issue:** fix #15173 | {
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` if file_path.lower().endswith(".xlsx") or file_path.lower().endswith(".xls"):
loader = UnstructuredExcelLoader(file_path, mode="elements")
document = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=10)
texts = text_splitter.split_documents(documents=document)`
what can i modify so that I get answer from the column?
### Suggestion:
_No response_ | {
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] | open | false | null | [] | null | 1 | 2024-01-05T08:21:08 | 2024-01-05T08:28:39 | null | NONE | null | ### System Info
I want to use the news-api tool, and I have these setting for api key:
```
os.environ["NEWS_API_KEY"] = "9ed***"
tools = load_tools(["news-api"], llm=llm, news_api_key="9ed****", memory=memory)
```
But when the action is activated, the error is:
```
Action: Search
https://newsapi.org/v2/top-headlines?country=us&category=sports&pageSize=2&apiKey=YOUR_API_KEY
Replace `YOUR_API_KEY` with your actual API key from NewsAPI.org to authenticate the request.
```
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. load news-api tool
2. run the tool in the agent
### Expected behavior
the news-api tool can run successfully | {
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] | null | 1 | 2024-01-05T07:58:17 | 2024-01-15T19:41:13 | null | NONE | null | **Description**
Support async streaming for VertexAI model. The async method implementations are now aligned with their non-async counterparts for consistency.
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1. **New Endpoint Addition**: Integration of a new endpoint to utilize completion models hosted on Konko.
2. **Chat Model Updates for Backward Compatibility**: We have updated the chat models to ensure backward compatibility with previous OpenAI versions.
4. **Updated Documentation**: Comprehensive documentation has been updated to reflect these new changes, providing clear guidance on utilizing the new features and ensuring seamless integration.
Thank you to the LangChain team for their exceptional work and for considering this PR. Please let me know if any additional information is needed. | {
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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:** Adding resource for Curie model
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** @mmarccode
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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] | open | false | null | [] | null | 1 | 2024-01-05T04:54:04 | 2024-01-05T05:01:50 | null | NONE | null | ### System Info
Python 3.11
Langchain 0.0.354
Windows 11
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Getting exception httpcore.UnsupportedProtocol: Request URL is missing an 'http://' or 'https://' protocol. while executing SQLDatabaseToolkit
Execute below code and described environment:
`import os
from dotenv import load_dotenv
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.chat_models import ChatOpenAI
llm_model = "gpt-3.5-turbo"
#load secrets and keys from .env
load_dotenv()
database_user_local = os.getenv("DATABASE_USERNAME_LOCAL")
database_password_local = os.getenv("DATABASE_PASSWORD_LOCAL")
database_server_local = os.getenv("DATABASE_SERVER_LOCAL")
database_db_local = os.getenv("DATABASE_DB_LOCAL")
llm = ChatOpenAI(temperature = 0.0, model_name=llm_model)
connection_string = f"mssql+pymssql://{database_user_local}:{database_password_local}@{database_server_local}/{database_db_local}"
db = SQLDatabase.from_uri(connection_string)
user_id = 118
query = "Select top 5 * from dbo.Users where Id = " + str(user_id)
toolkit = SQLDatabaseToolkit(db=db, llm=llm, reduce_k_below_max_tokens=True)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
input_variables=["query"]
)
agent_executor.run("Get lastlogin from dbo.Users for user_id 118")
`
Error:
` Entering new AgentExecutor chain...
Traceback (most recent call last):
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 67, in map_httpcore_exceptions
yield
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 231, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpcore\_sync\connection_pool.py", line 215, in handle_request
raise UnsupportedProtocol(
httpcore.UnsupportedProtocol: Request URL is missing an 'http://' or 'https://' protocol.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "D:\Program\Python\Python311\Lib\site-packages\openai\_base_client.py", line 866, in _request
response = self._client.send(request, auth=self.custom_auth, stream=stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 915, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 943, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 980, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 1016, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 230, in handle_request
with map_httpcore_exceptions():
File "D:\Program\Python\Python311\Lib\contextlib.py", line 155, in __exit__
self.gen.throw(typ, value, traceback)
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 84, in map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.UnsupportedProtocol: Request URL is missing an 'http://' or 'https://' protocol.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 67, in map_httpcore_exceptions
yield
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 231, in handle_request
resp = self._pool.handle_request(req)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpcore\_sync\connection_pool.py", line 215, in handle_request
raise UnsupportedProtocol(
httpcore.UnsupportedProtocol: Request URL is missing an 'http://' or 'https://' protocol.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "D:\Program\Python\Python311\Lib\site-packages\openai\_base_client.py", line 866, in _request
response = self._client.send(request, auth=self.custom_auth, stream=stream)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 915, in send
response = self._send_handling_auth(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 943, in _send_handling_auth
response = self._send_handling_redirects(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 980, in _send_handling_redirects
response = self._send_single_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_client.py", line 1016, in _send_single_request
response = transport.handle_request(request)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 230, in handle_request
with map_httpcore_exceptions():
File "D:\Program\Python\Python311\Lib\contextlib.py", line 155, in __exit__
self.gen.throw(typ, value, traceback)
File "D:\Program\Python\Python311\Lib\site-packages\httpx\_transports\default.py", line 84, in map_httpcore_exceptions`
### Expected behavior
It should execute sql agent and return the result | {
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] | closed | false | null | [] | null | 1 | 2024-01-05T04:47:17 | 2024-01-05T05:57:31 | 2024-01-05T05:57:31 | COLLABORATOR | null | there are still a few broken ones:
- some in the chains docs, which I will delete soon :)
- some pointing to a sqlite tool, which we should add | {
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] | closed | false | null | [] | null | 1 | 2024-01-05T03:34:14 | 2024-01-05T05:57:21 | 2024-01-05T05:57:21 | CONTRIBUTOR | null | **Description**: Fixed a minor typo in the RAG Docs:
- ~~This usually happen offline~~ -> This usually happen**s** offline | {
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