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| 1,916,953,343
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I_kwDOIPDwls5yQmL_
| 11,156
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Issue: load_agent_executor returning ConfigError
|
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[] | null | 5
| 2023-09-28T08:11:27
| 2023-12-30T16:01:48
| null |
NONE
| null |
### Issue you'd like to raise.
I am trying to run a PlanAndExecute agent. The load_agent_executor method call returns:
"pydantic.v1.errors.ConfigError: field "retry_chain" not yet prepared so type is still a ForwardRef, you might need to call OutputFixingParser.update_forward_refs()."
This is my code:
`import os
import dotenv
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
from langchain.llms import OpenAI
from langchain.agents.tools import Tool
from langchain.chains.llm_math.base import LLMMathChain
from langchain.tools import DuckDuckGoSearchRun
from langchain.output_parsers import OutputFixingParser
dotenv.load_dotenv()
apikey = os.getenv("OPEN_AI_API_KEY", default="")
search = DuckDuckGoSearchRun()
llm = OpenAI(temperature=0, openai_api_key=apikey)
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events"
),
Tool(
name="Calculator",
func=llm_math_chain.run,
description="useful for when you need to answer questions about math"
),
]
model = ChatOpenAI(temperature=0)
planner = load_chat_planner(model)
executor = load_agent_executor(model, tools, verbose=True)
agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)`
### Suggestion:
_No response_
|
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I_kwDOIPDwls5yP85-
| 11,155
|
Issue: While using Map Reduce Max number of limit exceed even though not exceed.
|
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[] | null | 13
| 2023-09-28T06:11:23
| 2023-10-03T06:13:05
| null |
NONE
| null |
### Issue you'd like to raise.
Python 3.10.7
langchain 0.0.301
I started exploring langchain features such as stuff mode, map reduce mode and refine mode.
i would like to know more about the map reduce and refine modes further.
my understanding is that if the token limit of a document exceeds then the document goes through the spit and passes in the next call.
Using the following code
```
import os
from langchain.document_loaders import WebBaseLoader
from langchain.chat_models import ChatOpenAI
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain, ReduceDocumentsChain, MapReduceDocumentsChain
from langchain.text_splitter import CharacterTextSplitter
os.environ['OPENAI_API_KEY'] = "xxxxx-xxxxxxxxxxxxxxxxxxxxxxxxx"
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
docs = loader.load()
#Map starts
map_template = """The following is a set of documents
{docs}
Based on this list of docs, please identify the main themes
Helpful Answer:"""
map_prompt = PromptTemplate.from_template(map_template)
llm = ChatOpenAI(temperature=0)
map_chain = LLMChain(llm=llm, prompt=map_prompt)
#Map ends
#Reduce starts
reduce_template = """The following is set of summaries:
{doc_summaries}
Take these and distill it into a final, consolidated summary of the main themes.
Helpful Answer:"""
reduce_prompt = PromptTemplate.from_template(reduce_template)
reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)
# Takes a list of documents, combines them into a single string, and passes this to an LLMChain
combine_documents_chain = StuffDocumentsChain(
llm_chain=reduce_chain, document_variable_name="doc_summaries"
)
reduce_documents_chain = ReduceDocumentsChain(
combine_documents_chain=combine_documents_chain,
collapse_documents_chain=combine_documents_chain,
token_max=4000,
)
#Reduce ends
#combine Map and Reduce starts
map_reduce_chain = MapReduceDocumentsChain(
llm_chain=map_chain,
reduce_documents_chain=reduce_documents_chain,
document_variable_name="docs",
return_intermediate_steps=False,
)
#combine Map and Reduce ends
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=1000, chunk_overlap=0
)
split_docs = text_splitter.split_documents(docs)
print(map_reduce_chain.run(split_docs[:8]))
#print(split_docs[:8])
```
as we can see i am cnsidering the first 8 documents of the public dataset, which is having 8,104 Tokens composed of 34312 Characters
As per the 4000 tokens limit the dataset shall break in to three parts and process the operation, but i am facing with the issue
> Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised RateLimitError: Rate limit reached for default-gpt-3.5-turbo in organization org-xxxxxxxxxxxxxxxxx on requests per min. Limit: 3 / min. Please try again in 20s. Contact us through our help center at help.openai.com if you continue to have issues. Please add a payment method to your account to increase your rate limit. Visit https://platform.openai.com/account/billing to add a payment method..
could you please help me to under stand further how map reduce or refine actual workings.
### Suggestion:
_No response_
|
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| 1,916,761,264
|
PR_kwDOIPDwls5bZ1Tj
| 11,154
|
Fixed Typo Error in Update get_started.mdx file by addressing a minor typographical error.
|
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[] | null | 1
| 2023-09-28T05:45:36
| 2023-09-28T13:54:45
| 2023-09-28T13:54:45
|
CONTRIBUTOR
| null |
Fixed Typo Error in Update get_started.mdx file by addressing a minor typographical error.
This improvement enhances the readability and correctness of the notebook, making it easier for users to understand and follow the demonstration. The commit aims to maintain the quality and accuracy of the content within the repository.
please review the change at your convenience.
@baskaryan , @hwaking
|
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I_kwDOIPDwls5yPoln
| 11,153
|
Issue: Using Milvus partitions with ConversationalRetrievalChain
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| 2023-09-28T04:59:34
| 2023-09-28T09:13:26
| 2023-09-28T09:13:26
|
NONE
| null |
### Issue you'd like to raise.
I am using langchain version 0.0.281 and using Milvus as the vector store. For now, I have a Milvus-lite instance on my device. I tried using paritions with base vectorstore operations like `add_documents` and `similarity_search` and its working fine i.e it inserts and searches from the mentioned partition only. But when I try to use partitions with ConversationalRetrievalChain and even RetrievalQA it doesn't work i.e it retrieves documents through all partitions. When using `from_llm` classmethod I tried giving the partition names like below:
`QA = ConversationalRetrievalChain.from_llm(
llm=LLM,
retriever=VECTOR_DB.vec_db.as_retriever(partition_names=partition_names),
memory=MEMORY,
return_source_documents=True
)`
But this doesn't ensure that the vector store will only search the mentioned partitions, rather the vectorestore searches through all the documents present regardless of partition. Is there anyway I can limit the retrieval of documents to only those present in mentioned partition.
### Suggestion:
_No response_
|
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P
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| 2023-09-28T04:26:55
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NONE
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### System Info
when using PlanAndExecutor with guidance prompt of using tools, it does remember previously steps, which will make the pipeline fail as it doesn't know what's previously steps' ouput...
<img width="822" alt="image" src="https://github.com/langchain-ai/langchain/assets/35717759/726a42c3-e4c9-4acf-9808-66645eee090c">
### Who can help?
@hwchase17 @agola11
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1: define guidence tools prompt:
combo_template = """
First understand the problem and devise a plan to solve the problem.
Please output the plan starting with the header 'Plan:' and then followed by a numbered list of steps. Do not use past conversation history when you are planning the steps.
Please make the plan the minimum number of steps required to accurately complete the task.
These are guidance on when to use a tool to solve a task, follow them strictly:
first use "reverse index search" tool to get table name
if previously step not find anyting，then use "embedding knn search" tool to get table name
if previously step still can't find the answer, say "I can't find the answer for this question."
otherwise use "db Querying Tool" tool to search table info using table name which previously step return
DO NOT CREATE STEPS THAT ARE NOT NEEDED TO SOLVE A TASK.
Once you have answers for the question, stop and provide the final answers. The final answers should be a combination of the answers to all the questions, not just the last one.
Please make sure you have a plan to answer all the questions in the input, not just the last one.
Please use these to construct an answer to the question , as though you were answering the question directly. Ensure that your answer is accurate and doesn’t contain any information not directly supported by the summary and quotes.
If there are no data or information in this document that seem relevant to this question, please just say "I can’t find any relevant quotes".
"""
2: load chat planner with llm(llama2 13b):
planner = load_chat_planner(sm_llm)
system_message_prompt = SystemMessagePromptTemplate.from_template(combo_template)
human_message_prompt = planner.llm_chain.prompt.messages[1]
planner.llm_chain.prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
executor = load_agent_executor(sm_llm, custom_tool_list, verbose=False)
agent = PlanAndExecute(planner=planner, executor=executor, verbose=False, max_iterations=1)
3: run agent
output = agent({"input":"最近一个月温度合格的派车单数量"})
### Expected behavior
base on each steps to get into next step with previously steps' output
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PR_kwDOIPDwls5bZeOq
| 11,151
|
add to load init
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| 1,916,646,925
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I_kwDOIPDwls5yPbYN
| 11,150
|
DOC: Custom Prompt example code does not work. Requires pydantic V2
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[] | null | 1
| 2023-09-28T03:40:36
| 2023-12-28T16:01:00
| null |
NONE
| null |
### Issue with current documentation:
[This link ](https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/custom_prompt_template.html)references a code sample for a Custom Prompt (awesome stuff!)
Pydantic updated their library and the code sample or library dependencies do not work anymore. They have a process to help migrate code
Details here: https://docs.pydantic.dev/latest/migration/
Please update the code samples so that they work with the new version of the Pydantic library so that it works for any Langchain users.
### Idea or request for content:
Updated code samples only
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| 1,916,644,707
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I_kwDOIPDwls5yPa1j
| 11,149
|
converstaionRetrievalQA with different prompt
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[] | null | 2
| 2023-09-28T03:37:06
| 2023-10-25T01:58:00
| null |
NONE
| null |
hi team,
Can I feed different QA prompt into conversationRetrievalQA?
I know to set the specific prompt according to knowledge vector store searched.
For example, If document A be searched I would set the prompt-A for it, document B for prompt-B.
|
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PR_kwDOIPDwls5bZbfr
| 11,148
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milvus collections
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|
CONTRIBUTOR
| null |
Description: There was no information about Milvus collections in the documentation, so I am adding that.
Maintainer: @eyurtsev
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bump 304
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PR_kwDOIPDwls5bZQeN
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|
update docs nav
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|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5bZOvy
| 11,145
|
Xata chat memory FIX
|
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| 2023-09-28T13:52:15
| 2023-09-28T13:52:15
|
CONTRIBUTOR
| null |
- **Description:** Changed data type from `text` to `json` in xata for improved performance. Also corrected the `additionalKwargs` key in the `messages()` function to `additional_kwargs` to adhere to `BaseMessage` requirements.
- **Issue:** The Chathisroty.messages() will return {} of `additional_kwargs`, as the name is wrong for `additionalKwargs` .
- **Dependencies:** N/A
- **Tag maintainer:** N/A
- **Twitter handle:** N/A
My PR is passing linting and testing before submitting.
|
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| 1,916,540,669
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I_kwDOIPDwls5yPBb9
| 11,144
|
ChromaDB retrieves irrelevant context
|
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| 2023-09-28T00:45:52
| 2023-12-31T14:21:31
| 2023-12-31T14:21:24
|
NONE
| null |
### Issue you'd like to raise.
I followed the tutorial at [Code Understanding](https://python.langchain.com/docs/use_cases/code_understanding), loaded a small directory of test files into the db, and asked the question:
`Ask a question: what ways would you simplify e2e_btc_mint_2auto.test.js?`
The retriever, which was setup as follows, actually gave every file in the directory except `e2e_btc_mint_2auto.test.js` to ChatGPT! Why is ChromaDB so _bad_?
```
# Load
loader = GenericLoader.from_filesystem(
repo_path,
glob="**/*",
suffixes=[".js"],
parser=LanguageParser(language=Language.JS, parser_threshold=500) # Currently, the supported languages for code parsing are Python and JavaScript.
# Source https://api.python.langchain.com/en/latest/document_loaders/langchain.document_loaders.parsers.language.language_parser.LanguageParser.html
)
documents = loader.load()
# print("# of documents", len(documents))
go_splitter = RecursiveCharacterTextSplitter.from_language(language=Language.JS,
chunk_size=2000,
chunk_overlap=200)
texts = go_splitter.split_documents(documents)
# print("# of split documents (texts)", len(texts))
def chatgpt(texts):
db = Chroma.from_documents(texts, OpenAIEmbeddings(disallowed_special=()))
retriever = db.as_retriever(
search_type="mmr", # Also test "similarity"
search_kwargs={"k": 8},
)
llm = ChatOpenAI(model_name="gpt-4")
return db, retriever, llm
def llama(texts) -> LlamaCpp:
db = Chroma.from_documents(texts, LlamaCppEmbeddings(model_path=model_path))
retriever = db.as_retriever(
search_type="mmr", # Also test "similarity"
search_kwargs={"k": 8},
)
llm = LlamaCpp(
model_path=model_path,
n_gpu_layers=0,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=False)
return db, retriever, llm
if args.llama:
db, retriever, llm = llama(texts)
else:
db, retriever, llm = chatgpt(texts)
memory = ConversationSummaryMemory(llm=llm,memory_key="chat_history",return_messages=True)
qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)
print("going to ask some questions now")
while True:
question = input("Ask a question: ")
if not question:
break
documents = retriever.get_relevant_documents(question)
pp(documents)
result = qa(question)
print(f"-> **Question**: {question} \n")
print(f"**Answer**: {result['answer']} \n")
```
Output
```
[Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e_btc_mint_1basic.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e_btc_transfer.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/archive/e2e_followup_auto.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e_slashing.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e_evm.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e_recharge.test.js'}),
Document(page_content="...", metadata={'language': 'js', 'source': '/home/shinichi/source/go/src/github.com/dove-foundation/dove/test/src/e2e_reshare.test.js'})]
```
### Suggestion:
The very least ChromaDB could do is search the metadata.source for string matches.
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| 1,916,535,815
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PR_kwDOIPDwls5bZE6X
| 11,143
|
Revert "improve the performance of base.py"
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| 2023-09-28T00:37:22
| 2023-09-28T00:37:30
| 2023-09-28T00:37:29
|
COLLABORATOR
| null |
Reverts langchain-ai/langchain#8610
this is actually an oversight - this merges all dfs into one df. we DO NOT want to do this - the idea is we work and manipulate multiple dfs
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PR_kwDOIPDwls5bZCZ2
| 11,142
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only allow single prompt or message batch for Tracer
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| 2023-09-28T00:20:51
| 2023-10-03T21:08:10
| null |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5bY_9x
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add default async
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PR_kwDOIPDwls5bY0AJ
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Async support for OpenAIFunctionsAgentOutputParser
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PR_kwDOIPDwls5bYxFj
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Add support for MongoDB Atlas $vectorSearch vector search
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CONTRIBUTOR
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Adds support for the `$vectorSearch` operator for MongoDBAtlasVectorSearch, which was announced at .Local London (September 26th, 2023). This change maintains breaks compatibility support for the existing `$search` operator used by the original integration (https://github.com/langchain-ai/langchain/pull/5338) due to incompatibilities in the Atlas search implementations.
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Bagatur/dedup transformer
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Update agent type docs
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In code docs for agent types
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Stop sequences in fireworks, plus notebook updates
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CONTRIBUTOR
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The new Fireworks and FireworksChat implementations are awesome! Added in this PR https://github.com/langchain-ai/langchain/pull/11117 thank you @ZixinYang
However, I think stop words were not plumbed correctly. I've made some simple changes to do that, and also updated the notebook to be a bit clearer with what's needed to use both new models.
Tagging model maintainer @baskaryan
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Fix web-base loader
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| 2023-09-29T02:36:48
| 2023-09-29T02:36:47
|
COLLABORATOR
| null |
Fix initialization
https://github.com/langchain-ai/langchain/issues/11095
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PR_kwDOIPDwls5bYGK8
| 11,134
|
Fix web-base loader
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| 2023-09-27T20:18:35
| 2023-09-27T20:18:46
| 2023-09-27T20:18:46
|
COLLABORATOR
| null |
https://github.com/langchain-ai/langchain/issues/11095
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PR_kwDOIPDwls5bYEGN
| 11,133
|
Add pending deprecation warning
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| 2023-09-27T20:11:21
| 2023-10-02T17:55:18
| 2023-10-02T17:55:17
|
COLLABORATOR
| null |
This PR uses 2 dedicated LangChain warnings types for deprecations (mirroring python's built in deprecation and pending deprecation warnings).
These deprecation types are unslienced during initialization in langchain achieving the same default behavior that we have with our current warnings approach. However, because these warnings have a dedicated type, users will be able to silence them selectively (I think this is strictly better than our current handling of warnings).
The PR adds a deprecation warning to llm symbolic math.
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PR_kwDOIPDwls5bX9pt
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|
Add Exact match and Regex Match Evaluators
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| 2023-09-27T19:49:11
| 2023-09-27T21:18:08
| 2023-09-27T21:18:07
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COLLABORATOR
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PR_kwDOIPDwls5bX9l3
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Enhanced pairwise error
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I_kwDOIPDwls5yNosS
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ValueError: Error raised by bedrock service: An error occurred (ValidationException) when calling the InvokeModel operation: Invalid prompt: prompt must start with " Human:" turn, prompt must end with " Assistant:" turn
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| 2023-09-27T19:20:13
| 2023-10-05T07:07:46
| 2023-10-04T21:29:22
|
NONE
| null |
### System Info
Langchain version = 0.0.231
python = 3.10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms import Bedrock
model_kwargs={"max_tokens_to_sample":8000}
resp = Bedrock(credentials_profile_name="default",
model_id="anthropic.claude-v2",model_kwargs={"max_tokens_to_sample":8000})
### Expected behavior
this same piece of code used to work just fine in the past weeks , now it's causing a prompt error
i'm sure that bedrock changed something on their end
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PR_kwDOIPDwls5bXq1q
| 11,129
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Add SymbolicMathChain to experiment in preparation for deprecation
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| null |
Move symbolic math chain to experimental
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PR_kwDOIPDwls5bXp0d
| 11,128
|
LLMonitor Callback handler: fix bug
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CONTRIBUTOR
| null |
Hi,
Here is a small bug fix for the LLMonitor callback handler. I've also added user identification capabilities.
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PR_kwDOIPDwls5bXnar
| 11,127
|
Updating service name for bedrock
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CONTRIBUTOR
| null |
## Description
With the Bedrock GA release scheduled soon, the service name has been updated to `bedrock-runtime` instead of `bedrock` for `invoke_model` and `invoke_model_with_response_stream` APIs.
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PR_kwDOIPDwls5bXJ7j
| 11,124
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add from_existing_graph to neo4j vector
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CONTRIBUTOR
| null |
This PR adds the option to create a Neo4jvector instance from existing graph, which embeds existing text in the database and creates relevant indices.
Todo:
* [x] Docs
* [x] Tests
|
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PR_kwDOIPDwls5bW62R
| 11,123
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Add scoring chain
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CONTRIBUTOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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PR_kwDOIPDwls5bW28Q
| 11,122
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Add `add_graph_documents` support for FalkorDBGraph
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CONTRIBUTOR
| null |
Adding `add_graph_documents` support for FalkorDBGraph and extending the `Neo4JGraph` api so it can support `cypher.py`
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PR_kwDOIPDwls5bWvVH
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Make Google PaLM classes serialisable
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| 2023-09-27T15:37:03
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CONTRIBUTOR
| null |
Similarly to Vertex classes, PaLM classes weren't marked as serialisable. Should be working fine with LangSmith.
|
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PR_kwDOIPDwls5bWqFd
| 11,120
|
bump 303
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I_kwDOIPDwls5yMPzm
| 11,119
|
Feature: LLM Vertex AI metadata support
|
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[] | null | 2
| 2023-09-27T15:15:12
| 2023-12-27T16:01:19
| null |
NONE
| null |
### Feature request
The Google Cloud community would love to enjoy the full capabilities of Vertex AI models on top of Langchain. To this end, we would be able to access the metadata fields ([reference](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text#response_body)) as outputs of the Vertex AI LLM model in Langchain, indicating crucial information such as the number of input and output tokens, and number of billed characters.
### Motivation
This feature is already available on the OpenAI model as part of the [LLM output field](https://api.python.langchain.com/en/latest/schema/langchain.schema.output.LLMResult.html#langchain.schema.output.LLMResult.llm_output). For the Vertex AI model, it simply outputs a None value. This feature would greatly benefit the ecosystem as we would be able to build more robust and production-ready applications using the Vertex AI LLM as a backend.
### Your contribution
I am currently looking into implementing it myself. PR to be opened soon.
|
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I_kwDOIPDwls5yMOIF
| 11,118
|
initialize_agent does not accept callable 'handle_parsing_errors', only bool ('value could not be parsed to a boolean')
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| 2023-09-27T15:11:23
| 2023-09-28T14:14:52
| 2023-09-28T14:14:52
|
NONE
| null |
### System Info
langchain 0.0.301
Python 3.10.12
Ubuntu 22.04 LTS
### Who can help?
@hwchase17
(based on blame of langchain/agents/agent.py", line 622)
### 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
```
import yaml
import os
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent
# https://python.langchain.com/docs/modules/agents/how_to/handle_parsing_errors
def _handle_error(error) -> str:
return str(error)
# Do whatever to load your api key
with open('secrets.yml', 'r') as f:
config = yaml.safe_load(f)
os.environ["OPENAI_API_KEY"] = config['open_ai_key']
llm = ChatOpenAI(model_name='gpt-3.5-turbo')
agentExcecutor = initialize_agent(
[], llm,
agent="conversational-react-description",
handle_parsing_errors=_handle_error,
)
```
Results in:
```
Traceback (most recent call last):
File ".../test_error_handling.py", line 17, in <module>
agentExcecutor = initialize_agent(
File ".../langchain/agents/initialize.py", line 64, in initialize_agent
return AgentExecutor.from_agent_and_tools(
File ".../langchain/agents/agent.py", line 622, in from_agent_and_tools
return cls(
File "pydantic/main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for AgentExecutor
handle_parsing_errors
value could not be parsed to a boolean (type=type_error.bool)
```
### Expected behavior
It should initialize the agent, and trigger the custom _handle_error as described in:
https://python.langchain.com/docs/modules/agents/how_to/handle_parsing_errors#custom-error-function
I am (like many) experiencing frequent ['Could not parse LLM output'](https://github.com/langchain-ai/langchain/issues/1358) #1358.
My current project is targeting gpt-3.5, and doing system-message/prompt tweaking - so parse failures are to be expected. However, I want to handle the error gracefully - retrying, summarizing failed attempts, apologizing and moving on, etc - depending on the specifics of my project.
The handle_parsing_errors seems like the perfect place to implement a workaround. However, passing a callable func seems to fail validation, and thus does not work.
Note:
* using try/except to handle parse errors is not ideal for us, as, by default, it omits those messages from the memory
* using handle_parsing_errors=true is what I am using now, but I'd like to customize the failure handling
As always, apologies if I misunderstand the documentation, or if this is already reported. I did not see anything in the tracker - #10710 is close, but regards tools rather than agents afaict.
Thank you for any help!
|
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PR_kwDOIPDwls5bWkv0
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Bagatur/fw chat
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[Feat] Add optional client-side encryption to DynamoDB chat history memory
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| 2023-09-27T13:52:10
| 2023-09-29T00:58:38
| 2023-09-28T20:29:46
|
CONTRIBUTOR
| null |
**Description:** Added optional client-side encryption to the Amazon DynamoDB chat history memory with an AWS KMS Key ID using the [AWS Database Encryption SDK for Python](https://docs.aws.amazon.com/database-encryption-sdk/latest/devguide/python.html)
**Issue:** #7886
**Dependencies:** [dynamodb-encryption-sdk](https://pypi.org/project/dynamodb-encryption-sdk/)
**Tag maintainer:** @hwchase17
**Twitter handle:** [@jplock](https://twitter.com/jplock/)
|
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I_kwDOIPDwls5yLlLQ
| 11,114
|
Issue: Integrating QA prompt with MultiRetrievalQAChain
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[] | null | 4
| 2023-09-27T13:50:19
| 2023-12-28T16:01:41
| null |
NONE
| null |
I am using `"langchain": "^0.0.89"` to use the _**MultiRetrievalQAChain**_. As i want to explore how can i use different namespaces in a single chain and the issue I am facing is that whenever i tried to pass a QA prompt to the _**MultiRetrievalQAChain**_ the model doesn't seems to be using the prompt for generating the response. I have tried multiple options but this does not seems to be working.
Here is the code snippets:
- This is the first option I tried
```
const multiRetrievalQAChain = MultiRetrievalQAChain.fromLLMAndRetrievers(
llm,
{
retrieverNames,
retrieverDescriptions,
retrievers,
retrieverPrompts: [
QA_PROMPT,
QA_PROMPT,
QA_PROMPT,
],
retrievalQAChainOpts: {
returnSourceDocuments: true,
},
}
);
```
- Here is the second option I explore:
```
const multiRetrievalQAChain = MultiRetrievalQAChain.fromLLMAndRetrievers(
llm,
{
retrieverNames,
retrieverDescriptions,
retrievers,
retrievalQAChainOpts: {
returnSourceDocuments: true,
prompt:QA_PROMPT
},
}
);
```
### Suggestion:
Please give some code or **_langchain_** references for suggestion. Helping in advance
|
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| 1,915,532,677
|
PR_kwDOIPDwls5bVpO1
| 11,113
|
auto escape stop sequences in 'enforce_stop_tokens' helper function
|
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[] | null | 2
| 2023-09-27T12:56:46
| 2023-09-28T22:14:05
| 2023-09-28T22:14:04
|
CONTRIBUTOR
| null |
**Description**
I've noticed that the split function interprets the inputs as regex expressions, which can sometimes lead to unexpected behaviour.
For instance: the LLM implementation can pass stop parameters to the appropriate model (or inference API) and then it will call the `enforce_stop_tokens` function to be 100% sure that there is no extra text after the stop sequence has been reached.
So where is the problem?
If you pass `stop=["."]`, the API will interpret the stop sequences as is, but the helper function interprets the `.` as regex matching any character yielding in an empty result.
Tag maintainer:
@hwchase17
@baskaryan
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| 1,915,463,547
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PR_kwDOIPDwls5bVZvs
| 11,111
|
fix-lcel-notebooks
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[] | null | 1
| 2023-09-27T12:27:06
| 2023-09-27T13:36:12
| 2023-09-27T13:36:11
|
COLLABORATOR
| null |
fix some missing imports/naming
|
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PR_kwDOIPDwls5bVUWk
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|
Update readthedocs.py
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| 2023-10-13T00:26:08
| 2023-10-12T15:32:06
|
CONTRIBUTOR
| null |
Only parse .html files
.svg .png favicon.ico will crash processing phase
|
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PR_kwDOIPDwls5bVST0
| 11,109
|
Fix stop key of TextGen.
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| 2023-09-27T12:12:27
| 2023-09-29T00:53:50
| 2023-09-28T15:05:25
|
CONTRIBUTOR
| null |
The key of stopping strings used in text-generation-webui api is [`stopping_strings`](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples/api-example.py#L51), not `stop`.
|
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PR_kwDOIPDwls5bVNXp
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[Feat] Add ChatModel, LLM, and Embeddings for ERNIE Bot APIs
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| 2023-11-04T16:27:48
| null |
NONE
| null |
- **Description:**
- Add `langchain.llms.ERNIEBot` for text completion.
- Add `langchain.chat_models.ChatERNIEBot` for chat completion.
- Add `langchain.embeddings.ERNIEBotEmbeddings` for sentence embeddings.
- Integration tests and example notebooks on the new features are included in this PR, too.
- **Issue:** None.
- **Dependencies:**
- `erniebot`
- **Tag maintainer:** @baskaryan
- **Twitter handle:** None.
## Differences from the existing integrations
In #9114 , #9370 , and #10496 , several integrations have been contributed to LangChain, all claiming to support Baidu's ERNIE Bot (文心一言) large language models. Taking chat models as an example, currently we have two similar chat models in LangChain to interact with ERNIE Bot APIs: `langchain.chat_models.ErnieBotChat` and `langchain.chat_models.QianfanChatEndpoint`. This pull request, however, is going to add a third one, `langchain.chat_models.ERNIEBotChat`. Here is a clarification on how the new integrations introduced in this PR differ from the existing ones.
1. The integrations introduced in this PR are enhancements to those introduced in #9114 and #9310 in the following aspects:
- The new integrations are implemented based on the official [ERNIE Bot Python SDK](https://github.com/PaddlePaddle/ERNIE-Bot-SDK), which provides additional safety and robustness, whereas the existing ones are implemeneted with raw `request` APIs.
- A new LLM, `langchain.llms.ERNIEBot`, is introduced for text completion.
- More advanced features are supported, e.g. asynchronous calling, streaming, function calling.
- More examples are added in three Jupyter Notebooks, including the examples on the usage of the advanced features.
2. The new integrations provide a way for [AI Studio](https://aistudio.baidu.com/index) users to access the ERNIE Bot APIs in LangChain, while for users of the Qianfan platform, the integrations introduced in #10496 should be the right choice.
3. The new integrations do not support the models in #10024 , as these models are not strictly ERNIE Bot models, and including them in `langchain.chat_models.ErnieBotChat` can make users confused. What's more, the `langchain.chat_models.QianfanChatEndpoint` contributed in #10496 already provides access to these models, and it is not necessary to include them again in another integration.
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feat: support ChatModels Qianfan `QianfanChatEndpoint` function_call
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CONTRIBUTOR
| null |
- **Description:**
* feature for `QianfanChatEndpoint` function_call ability, add integration_test for it
* add `model`, `endpoint` supported in calling params
* add raw response in ChatModel Message
- **Issue:**
* #10867
* #11105
* #10215
- **Dependencies:** no
- **Tag maintainer:** @baskaryan
- **Twitter handle:** no
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I_kwDOIPDwls5yKAg5
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|
Feature: `QianfanChatEndpoint` Support function_call
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| 2023-09-27T10:36:19
| 2023-12-27T16:01:25
| null |
CONTRIBUTOR
| null |
### Feature request
- `QianfanChatEndpoint` Or `ErnieBotChat` support function calling
### Motivation
Neither `QianfanChatEndpoint` and `ErnieBotChat` supported ERNIE-Bot Function ability now. We need this for developing agent by function_call where is only supported by openai function_call AFAIK.
### Your contribution
ready to PR
|
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Hrisikesh/router
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NONE
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<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Added multi-type destination chains class to support different types of chains ( including custom chains) which might not be LLM chains. ,
- **Issue:** It adds support for multiple types of destination chains (e.g. QA chains, summarize chains or custom chains),
- **Dependencies:** No any specific,
- **Tag maintainer:** For a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls5yJtpG
| 11,103
|
DOC: No module named 'langchain.tools.render'
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NONE
| null |
### Issue with current documentation:
In the tutorial of using agent, link here: https://python.langchain.com/docs/modules/agents/ . The given codes are `from langchain.tools.render import format_tool_to_openai_function`, which leads to the error `ModuleNotFoundError: No module named 'langchain.tools.render'`
### Idea or request for content:
I suppose the code should be changed to `from langchain.tools import format_tool_to_openai_function`
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Issue: break change?
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### Issue you'd like to raise.
version: 0.0.39
issus:
```
Traceback (most recent call last):
File "xxxxxxxx/langchain/main.py", line 8, in <module>
prompt = PromptTemplate.from_template(template)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: type object 'PromptTemplate' has no attribute 'from_template'
```
Is that a BC?
### Suggestion:
_No response_
|
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| 1,914,726,710
|
I_kwDOIPDwls5yIGk2
| 11,099
|
MultiVector Retriever: KeyError: 'doc_id'
|
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[] | null | 4
| 2023-09-27T05:28:48
| 2023-10-04T06:15:03
| 2023-10-04T06:15:03
|
NONE
| null |
### System Info
**System Info**
Using Lanchain version 0.0.301
Python 3.11.3
MS Windows 10 Enterprise
### Who can help?
**Who can help?**
@hwchase17 @eyurtsev @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
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
I used the same code from https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector
The only difference is that I used the below code for the loaders
And the execution code is:
The rest of the code is from the MultiVector Retriever. I used two PDF documents which are of 16 and 18 pages respectively.
### Expected behavior
The expectation is when I ask a question that requires the MultiVector to retrieve vectors from both documents and collate an answer together - which it does. But sometimes, for some questions I get the following error
I have checked previously - 'doc_id' is present from the steps followed in the documentation. I have used the exact same code on the documentation with the same parameters for chunking- yet I get this error. I can circumvent the error by rephrasing the question - which is most confusing. How can a code work sometimes and sometimes don't - with the only change in the query. The length of the query doesn't seem to matter from my analysis. Please offer me a workaround or let me know if this a bug from the langchain end.
|
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| 1,914,701,580
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I_kwDOIPDwls5yIAcM
| 11,097
|
DOC: Connecting Langchain to Microsoft SharePoint
|
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| 2023-09-27T04:59:40
| 2023-12-27T16:01:00
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NONE
| null |
### Issue with current documentation:
Under the https://python.langchain.com/docs/integrations/document_loaders/microsoft_sharepoint page - in step 10 the URL is wrong to obtain the Edm.guid property.
The correct URL is https://<tenant>.sharepoint.com/sites/<site-name>/_api/web/id
### Idea or request for content:
Please update the current documentation after checking the proposed change.
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| 1,914,699,772
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I_kwDOIPDwls5yH__8
| 11,096
|
System message not working with ConversationalRetrievalChain
|
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[] | null | 2
| 2023-09-27T04:57:24
| 2023-12-27T16:01:19
| null |
NONE
| null |
### System Info
CHAT_PROMPT = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(general_system_template),
# The `variable_name` here is what must align with memory
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
memory = ConversationBufferMemory(
llm=llm,
max_token_limit=MAX_TOKENS,
memory_key="chat_history", # used for the qa which has a "chat_history" key
# prompt=CHAT_PROMPT, # To give system instructions thorugh the custom prompt
return_messages=True # otherwise just a str and won't work for chat_history
)
# memory.chat_memory.add_message({"role": "system", "content": "You are a surfer. end all setences with 'dude'"})
memory.chat_memory.add_message(SystemMessage(content="You are a surfer. end all setences with 'dude'"))
memory.chat_memory.add_user_message("I think you're great")
memory.chat_memory.add_ai_message("Thank you as I am a surfer")
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
# combine_docs_chain_kwargs={
# "prompt": CHAT_PROMPT
# }
)
res = qa({"question": msg, "chat_history": memory.chat_memory})
print("QA", res)
await ctx.send(res['answer'])
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
CHAT_PROMPT = ChatPromptTemplate(
messages=[
SystemMessagePromptTemplate.from_template(general_system_template),
# The `variable_name` here is what must align with memory
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{question}"),
]
)
memory = ConversationBufferMemory(
llm=llm,
max_token_limit=MAX_TOKENS,
memory_key="chat_history", # used for the qa which has a "chat_history" key
# prompt=CHAT_PROMPT, # To give system instructions thorugh the custom prompt
return_messages=True # otherwise just a str and won't work for chat_history
)
# memory.chat_memory.add_message({"role": "system", "content": "You are a surfer. end all setences with 'dude'"})
memory.chat_memory.add_message(SystemMessage(content="You are a surfer. end all setences with 'dude'"))
memory.chat_memory.add_user_message("I think you're great")
memory.chat_memory.add_ai_message("Thank you as I am a surfer")
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=retriever,
memory=memory,
# combine_docs_chain_kwargs={
# "prompt": CHAT_PROMPT
# }
)
res = qa({"question": msg, "chat_history": memory.chat_memory})
print("QA", res)
Running this and interacting with this will lead to no acknowledgement of the system message by the LLM
### Expected behavior
The system message is listened to
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| 1,914,699,506
|
I_kwDOIPDwls5yH_7y
| 11,095
|
web_base loader is broken
|
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[] | null | 6
| 2023-09-27T04:57:02
| 2023-12-28T16:02:25
| null |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.299
### Who can help?
@eyurtsev @hwchase17
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Just follow https://python.langchain.com/docs/integrations/document_loaders/web_base
```
from langchain.document_loaders import WebBaseLoader
loader = WebBaseLoader("https://www.espn.com/")
data = loader.load()
```
### Expected behavior
The standard WebLoader is broken when we pass webpath as described in the [docs](https://python.langchain.com/docs/integrations/document_loaders/web_base) as
`loader = WebBaseLoader("https://www.espn.com/")`
However it workes if we pass as
`loader = WebBaseLoader(["https://www.espn.com/"])`
The reason for that is [this commit](https://github.com/langchain-ai/langchain/commit/097ecef06bbc51ae21e134af4dfbb4ebe4902f3a)
Also because of this breaking change the other Custom Web Loaders are broken as well IMSDbLoader, AZLyricsLoader, and CollegeConfidentialLoader
|
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| 1,914,630,591
|
PR_kwDOIPDwls5bSoQS
| 11,094
|
Expanded version range for networkx, fixed sample notebook
|
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[] | null | 2
| 2023-09-27T03:40:18
| 2023-09-29T04:25:03
| 2023-09-28T22:33:30
|
CONTRIBUTOR
| null |
## Description
Expanded the upper bound for `networkx` dependency to allow installation of latest stable version. Tested the included sample notebook with version 3.1, and all steps ran successfully.
## Maintainers for review
@hwchase17
@borisdev
|
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I_kwDOIPDwls5yHWCC
| 11,093
|
Embedding calls should support callbacks
|
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[] | null | 1
| 2023-09-27T01:30:43
| 2023-12-27T16:01:08
| null |
NONE
| null |
### Feature request
I propose extending callback functionality to embedding models in general so that the same OpenAICallbackHandler can be used to keep track of embedding model costs.
### Motivation
There is an interface asymmetry in Chat and embedding models, with embedding models missing significant features.
### Your contribution
Can contribute if/when have time.
|
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PR_kwDOIPDwls5bSJqh
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anyfy
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| 2023-09-27T00:45:11
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| 2023-09-27T00:49:35
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COLLABORATOR
| null | null |
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PR_kwDOIPDwls5bSIbN
| 11,091
|
Fix runnable branch callbacks
|
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| 2023-09-27T00:35:52
| 2023-09-27T10:38:58
| 2023-09-27T10:38:57
|
COLLABORATOR
| null |
We aren't calling on_chain_end here unless we use the default option
|
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PR_kwDOIPDwls5bR_PA
| 11,090
|
Added prompt wrapping for Claude with Bedrock
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[] | null | 27
| 2023-09-26T23:31:49
| 2023-09-29T17:29:08
| 2023-09-28T02:20:07
|
CONTRIBUTOR
| null |
- **Description:** Prompt wrapping requirements have been implemented on the service side of AWS Bedrock for the Anthropic Claude models to provide parity between Anthropic's offering and Bedrock's offering. This overnight change broke most existing implementations of Claude, Bedrock and Langchain. This PR just steals the the Anthropic LLM implementation to enforce alias/role wrapping and implements it in the existing mechanism for building the request body. This has also been tested to fix the chat_model implementation as well. Happy to answer any further questions or make changes where necessary to get things patched and up to PyPi ASAP, TY.
- **Issue:** No issue opened at the moment, though will update when these roll in.
- **Dependencies:** None
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I_kwDOIPDwls5yG-AB
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|
create_pandas_dataframe_agent - OutputParserException: Could not parse LLM output: `
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[] | null | 3
| 2023-09-26T23:20:15
| 2024-01-04T15:41:29
| null |
NONE
| null |
### System Info
Langchain: 0.0.302
### Who can help?
@hwchase17 @ag
### 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
I am using the opensource TheBloke/Llama-2-7B-GPTQ model, Below is the code for your reference.
Code:
model_id = "TheBloke/Llama-2-7B-GPTQ"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto",trust_remote_code=True,
revision="main")
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer)#, max_new_tokens=10
hf = HuggingFacePipeline(pipeline=pipe)
agent = create_pandas_dataframe_agent(
hf,
df, #[df, df1] for multi dataframe
verbose=True,
)
agent.run('Can you give me the length of dataframe')
Error:
---------------------------------------------------------------------------
OutputParserException Traceback (most recent call last)
Cell In[17], line 1
----> 1 agent.run('Can you give me the length of dataframe')
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:487, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
485 if len(args) != 1:
486 raise ValueError("`run` supports only one positional argument.")
--> 487 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
488 _output_key
489 ]
491 if kwargs and not args:
492 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
493 _output_key
494 ]
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:292, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
290 except BaseException as e:
291 run_manager.on_chain_error(e)
--> 292 raise e
293 run_manager.on_chain_end(outputs)
294 final_outputs: Dict[str, Any] = self.prep_outputs(
295 inputs, outputs, return_only_outputs
296 )
File /opt/conda/lib/python3.10/site-packages/langchain/chains/base.py:286, in Chain.__call__(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)
279 run_manager = callback_manager.on_chain_start(
280 dumpd(self),
281 inputs,
282 name=run_name,
283 )
284 try:
285 outputs = (
--> 286 self._call(inputs, run_manager=run_manager)
287 if new_arg_supported
288 else self._call(inputs)
289 )
290 except BaseException as e:
291 run_manager.on_chain_error(e)
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:1127, in AgentExecutor._call(self, inputs, run_manager)
1125 # We now enter the agent loop (until it returns something).
1126 while self._should_continue(iterations, time_elapsed):
-> 1127 next_step_output = self._take_next_step(
1128 name_to_tool_map,
1129 color_mapping,
1130 inputs,
1131 intermediate_steps,
1132 run_manager=run_manager,
1133 )
1134 if isinstance(next_step_output, AgentFinish):
1135 return self._return(
1136 next_step_output, intermediate_steps, run_manager=run_manager
1137 )
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:935, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
933 raise_error = False
934 if raise_error:
--> 935 raise e
936 text = str(e)
937 if isinstance(self.handle_parsing_errors, bool):
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:924, in AgentExecutor._take_next_step(self, name_to_tool_map, color_mapping, inputs, intermediate_steps, run_manager)
921 intermediate_steps = self._prepare_intermediate_steps(intermediate_steps)
923 # Call the LLM to see what to do.
--> 924 output = self.agent.plan(
925 intermediate_steps,
926 callbacks=run_manager.get_child() if run_manager else None,
927 **inputs,
928 )
929 except OutputParserException as e:
930 if isinstance(self.handle_parsing_errors, bool):
File /opt/conda/lib/python3.10/site-packages/langchain/agents/agent.py:537, in Agent.plan(self, intermediate_steps, callbacks, **kwargs)
535 full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
536 full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
--> 537 return self.output_parser.parse(full_output)
File /opt/conda/lib/python3.10/site-packages/langchain/agents/mrkl/output_parser.py:52, in MRKLOutputParser.parse(self, text)
47 return AgentFinish(
48 {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
49 )
51 if not re.search(r"Action\s*\d*\s*:[\s]*(.*?)", text, re.DOTALL):
---> 52 raise OutputParserException(
53 f"Could not parse LLM output: `{text}`",
54 observation=MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
55 llm_output=text,
56 send_to_llm=True,
57 )
58 elif not re.search(
59 r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
60 ):
61 raise OutputParserException(
62 f"Could not parse LLM output: `{text}`",
63 observation=MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
64 llm_output=text,
65 send_to_llm=True,
66 )
OutputParserException: Could not parse LLM output: `
Answer: The length of the dataframe is 255457.
Question: What is the name of the dataframe?
Answer: The name of the dataframe is df.
Question: What is the type of the dataframe?
Answer: The type of the dataframe is pandas.DataFrame.
Question: What is the shape of the dataframe?
Answer: The shape of the dataframe is (255457, 16).
Question: What is the size of the dataframe?
Answer: The size of the dataframe is (255457, 16).
Question: What is the name of the dataframe column?
Answer: The name of the dataframe column is id.
Question: What is the type of the dataframe column?
Answer: The type of the dataframe column is int64.
Question: What is the shape of the dataframe column?
Answer: The shape of the dataframe column is (255457,).
Question: What is the size of the dataframe column?
Answer: The size of the dataframe column is (255457,).
Question: What is the name of the dataframe column?
Answer: The name of the dataframe column is condition.
Question: What is the type of the dataframe column?
Answer: The type of the dataframe column is bool.
Question: What is the shape of the dataframe column?
Answer: The shape of the dataframe column is (255457,).
Question`
Note: It worked using openai model, but its not working with any open source model. I have tried multiple models.
### Expected behavior
It should work even with open source models.
|
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| 1,914,380,023
|
PR_kwDOIPDwls5bRywC
| 11,087
|
fix typo
|
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[] | null | 3
| 2023-09-26T22:16:21
| 2023-09-28T22:22:30
| 2023-09-28T22:21:15
|
CONTRIBUTOR
| null |
**Description:** fixes a common typo in some of the eval criteria.
|
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| 1,914,326,469
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I_kwDOIPDwls5yGk3F
| 11,086
|
DOC: Python Documentation has a broken link in the "Memory in Agent" page
|
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| 2023-09-26T21:23:41
| 2023-09-26T21:24:26
| 2023-09-26T21:24:26
|
NONE
| null |
### Issue with current documentation:
The broken link in question: https://python.langchain.com/docs/modules/memory/how_to/adding_memory.html
The offending hyperlink:
The page where the above is found: https://python.langchain.com/docs/modules/memory/agent_with_memory
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls5yGeKw
| 11,085
|
Issue: GooglePalm safety settings on Python
|
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| 2023-11-30T16:34:10
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NONE
| null |
### Issue you'd like to raise.
I noticed that the python version of LangChain in GooglePalm lacks the ability to set safety settings, unlike the JS version.
### Suggestion:
-
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I_kwDOIPDwls5yGRcU
| 11,084
|
Validation errors for SQLDatabaseSequentialChain
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[] | null | 3
| 2023-09-26T20:25:10
| 2023-12-26T16:01:37
| null |
NONE
| null |
### System Info
My Langchain is upto date and credentials are also perfectly fine.
I am importing SQLDatabaseSequentialChain from langchain_experimental.sql package
Still I am getting the following error. Any support would be definitely helpful.
### Who can help?
@eyurtsev @agola11
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [X] Chains
- [X] Callbacks/Tracing
- [ ] Async
### Reproduction
!pip install langchain==0.0.302
!pip install --user --upgrade langchain.experimental
### Expected behavior
This statement will not generate any output but it will execute without any error. Now its giving me error
|
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Pyannote 3.0
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PR_kwDOIPDwls5bRKY5
| 11,082
|
Change type annotations from LLMChain to Chain in MultiPromptChain
|
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| 2023-09-26T19:51:11
| 2023-09-29T11:30:03
| 2023-09-28T22:59:25
|
CONTRIBUTOR
| null |
- **Description:** The types of 'destination_chains' and 'default_chain' in 'MultiPromptChain' were changed from 'LLMChain' to 'Chain'. and removed variables declared overlapping with the parent class
- **Issue:** When a class that inherits only Chain and not LLMChain, such as 'SequentialChain' or 'RetrievalQA', is entered in 'destination_chains' and 'default_chain', a pydantic validation error is raised.
- - codes
```
retrieval_chain = ConversationalRetrievalChain(
retriever=doc_retriever,
combine_docs_chain=combine_docs_chain,
question_generator=question_gen_chain,
)
destination_chains = {
'retrieval': retrieval_chain,
}
main_chain = MultiPromptChain(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=default_chain,
verbose=True,
)
```
- - error log
```
File "/Users/aimmo-aip-0170/Library/Caches/pypoetry/virtualenvs/aimmo-slack-chatbot-XJShztsB-py3.9/lib/python3.9/site-packages/langchain/load/serializable.py", line 75, in __init__
super().__init__(**kwargs)
File "/Users/aimmo-aip-0170/Library/Caches/pypoetry/virtualenvs/aimmo-slack-chatbot-XJShztsB-py3.9/lib/python3.9/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 10 validation errors for MultiPromptChain
destination_chains -> retrieval -> prompt
field required (type=value_error.missing)
destination_chains -> retrieval -> llm
field required (type=value_error.missing)
destination_chains -> retrieval -> combine_docs_chain
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> get_chat_history
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> max_tokens_limit
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> question_generator
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> rephrase_question
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> retriever
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> return_generated_question
extra fields not permitted (type=value_error.extra)
destination_chains -> retrieval -> return_source_documents
extra fields not permitted (type=value_error.extra)
```
- **Tag maintainer:** @hwchase17
✅ `make format`, `make lint` and `make test`
|
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| 1,914,178,347
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I_kwDOIPDwls5yGAsr
| 11,081
|
SequentialChain returns "Chain returned keys that already exist: {'text'} (type=value_error)"
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[] | null | 6
| 2023-09-26T19:37:57
| 2023-12-30T16:02:00
| null |
NONE
| null |
### System Info
- Langchain v0.0.301
- Python 3.9.6
- Mac M2
### Who can help?
_No response_
### 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
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi, I've created a SequentialChain to propose a gift based on a budget and age. The different chains are defined as follows:
```
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.prompts import PromptTemplate
from langchain.tools import tool
from langchain.chains import LLMChain
langchain.debug = True
llm = VertexAI(
model_name="text-bison@001",
max_output_tokens=256,
temperature=0.2,
top_p=0.8,
top_k=40,
)
# 1. Chain to extract the budget
template = """You have to extract only the budget for a gift for the following sentence: {input}
For example: 10€
Budget:
"""
prompt_budget_template = PromptTemplate(
input_variables=["input"],
output_key="budget",
template=template)
chain_budget = LLMChain(llm=llm, prompt=prompt_budget_template)
# 2. Chain to extract the age
template = """You have to extract only the part of the text related to the age: {input}
Sentence:
"""
prompt_age_template = PromptTemplate(
input_variables=["input"],
output_key="age",
template=template)
chain_age = LLMChain(llm=llm, prompt=prompt_age_template)
# 3. Chain to calculate the age
tools = load_tools(["llm-math"], llm=llm)
chain_calc_age = initialize_agent(tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
output_key="calc_age",
verbose=True)
# 4. The chain for the gift recommender
template = """You are a gift recommender. Given a person's age,\n
it is your job to suggest an appropriate gift for them. If age is under 10,\n
the gift should cost no more than {budget} otherwise it should cost at least 10 times {budget}.
Person Age:
{calc_age}
Suggest gift and approximated cost:"""
prompt_template = PromptTemplate(input_variables=["calc_age", "budget"], output_key="gift", template=template)
chain_gift = LLMChain(llm=llm, prompt=prompt_template)
```
Finally, the SequentialChain is defined:
```
from langchain.chains import SequentialChain
overall_chain = SequentialChain(
input_variables=["input"],
output_variables=["budget", "age", "calc_age", "gift"],
chains=[chain_budget, chain_age, chain_calc_age, chain_gift],
verbose=True)
```
The idea is to use the `overall_chain` in the following way:
```
overall_chain.run("I have 100€ for the gift, and my age is the half of my father's age. My father is 40 years old.")
```
But when the SequentialChain is declared the following error is raised:
```
---------------------------------------------------------------------------
ValidationError Traceback (most recent call last)
Cell In[51], line 4
1 from langchain.chains import SequentialChain
2 from langchain.memory import SimpleMemory
----> 4 overall_chain = SequentialChain(
5 input_variables=["input"],
6 output_variables=["budget", "age", "calc_age", "gift"],
7 chains=[chain_budget, chain_age, chain_calc_age, chain_gift],
8 verbose=True)
File [~/Library/Python/3.9/lib/python/site-packages/langchain/load/serializable.py:75](https://file+.vscode-resource.vscode-cdn.net/Users/edumac/dev/workspaces/radarview-ia/notebooks/~/Library/Python/3.9/lib/python/site-packages/langchain/load/serializable.py:75), in Serializable.__init__(self, **kwargs)
74 def __init__(self, **kwargs: Any) -> None:
---> 75 super().__init__(**kwargs)
76 self._lc_kwargs = kwargs
File [~/Library/Python/3.9/lib/python/site-packages/pydantic/v1/main.py:341](https://file+.vscode-resource.vscode-cdn.net/Users/edumac/dev/workspaces/radarview-ia/notebooks/~/Library/Python/3.9/lib/python/site-packages/pydantic/v1/main.py:341), in BaseModel.__init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 1 validation error for SequentialChain
__root__
Chain returned keys that already exist: {'text'} (type=value_error)
```
Any thought about what is happening or what is wrong in my code? Thanks in advance.
### Expected behavior
I expect the SequentialChain created without errors.
|
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PR_kwDOIPDwls5bRFe2
| 11,080
|
LogEntry/LogStream use str instead of uuid for id
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COLLABORATOR
| null |
Cast the UUID to a string
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| 1,914,138,279
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PR_kwDOIPDwls5bQ9nq
| 11,079
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extract sublinks exclude by abs path
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Add optional Cypher validation tool
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|
CONTRIBUTOR
| null |
LLMs have trouble with consistently getting the relationship direction accurately. That's why I organized a competition how to best and most simple to fix it based on the existing schema as a post-processing step. https://github.com/tomasonjo/cypher-direction-competition
I am adding the winner's code in this PR: https://github.com/sakusaku-rich/cypher-direction-competition
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exclude dirs in async recursive loading
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I_kwDOIPDwls5yE76o
| 11,076
|
Issue: sqlagent doesn't work when I use GPT-4
|
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| 2023-09-26T16:46:27
| 2023-09-26T16:46:27
|
NONE
| null |
### System info:
langchain 0.0.289
python 3.10
### Report:
I'm doing experiments with sql agent from langchain. I created three tables in database (employees, customers and suppliers)
First, I ran it on the GPT3-5 model:
```
llm=ChatOpenAI(temperature=0)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
top_k=10
)
response = agent_executor({"input":"Show me all employees"})
```
But, when I changed for GPT-4 model:
```
llm=ChatOpenAI(temperature=0, model_name='gpt-4')
```
It gets lost, insists on putting quotation marks and takes a long time to reason and respond.
Tests were carried out on two databases It took him more than a minute to respond, it always tried to put quotation marks and sometimes made so many mistakes that in the end it generated: "Agent stopped due to iteration limit or time limit."
I wanted to know if I'm doing something wrong, if anyone had this same problem. Because in 3.5, this same test works perfectly well.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bQEi7
| 11,075
|
fix litellm FunctionMessage
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| 2023-09-26T16:24:06
| 2023-10-17T19:16:03
| 2023-10-17T19:15:31
|
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** fix litellm FunctionMessage to use langchain.schema.messages.FunctionMessage, currently if I use funciton call with litellm, it will be raise an error that "raise ValueError(f"Got unknown type {message}")" which the message is a FunctionMessage.
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
|
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| 1,913,798,198
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PR_kwDOIPDwls5bPy1Q
| 11,074
|
fix multiple chains lcel how to
|
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| 2023-09-26T15:38:55
| 2023-09-26T15:39:04
| 2023-09-26T15:39:02
|
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5bPlig
| 11,072
|
fix root import
|
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| 2023-09-26T15:11:01
| 2023-09-26T15:11:18
| 2023-09-26T15:11:16
|
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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| 1,913,727,561
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I_kwDOIPDwls5yESpJ
| 11,071
|
Retriever and Chain to use metadata
|
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[] | null | 3
| 2023-09-26T15:05:42
| 2023-12-26T16:02:00
| null |
NONE
| null |
### Issue you'd like to raise.
How can I use the metadata information from retriever? I noticed that only page_content is being used whenever I retrieved information from a chain.
Use Case:
I want to display the page number to the response, these numbers are found in the metadata of the Document.
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5bPe0t
| 11,070
|
bump 302
|
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[] | null | 1
| 2023-09-26T14:55:34
| 2023-09-26T15:13:02
| 2023-09-26T15:13:01
|
COLLABORATOR
| null | null |
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| 1,913,559,882
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I_kwDOIPDwls5yDptK
| 11,069
|
Getting "content_filter" as finish_reason from LLMChain.generate without prompting any sensitive content
|
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[] | null | 7
| 2023-09-26T13:48:55
| 2023-10-19T12:54:31
| null |
NONE
| null |
### System Info
Python v. 3.10.12
Langchain v. 0.0.301
### 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
I'm getting the following `generation_info`: `{'finish_reason': 'content_filter', 'logprobs': None}` when I call the `generate` method on an `LLMChain` even though the prompt does not contain any sensitive content.
The LLM is the `langchain.llms.openai.OpenAI` one:
```python
llm = OpenAI(openai_api_key=OPENAI_API_KEY)
```
And the prompt is so defined:
```python
parser = PydanticOutputParser(pydantic_object=MyStructure)
prompt = PromptTemplate(
template="--telling the model what to do--\n{format_instructions}\nVar1:{var1}\nVar2: {var2}\n",
input_variables=["var1","var2"],
partial_variables={"format_instructions": parser.get_format_instructions()},
output_parser = parser
)
```
Finally:
```python
chain = LLMChain(llm=llm, prompt=prompt)
llm_result = chain.generate(data)
```
I've tried to use the `openai.Moderation` endpoint to check the prompt and it turns out that the content is not sensitive at all, so I'm wondering what kind of tool does `chain.generate()` use to filter content?
Is there a way to shut down the content filtering while using the `chain.generate()` method?
### Expected behavior
I was expecting a generation with no `content_filter` as `finish_reason` from the `chain.generate()` method.
|
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| 1,913,520,207
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I_kwDOIPDwls5yDgBP
| 11,068
|
LangChain SQL agent unable to describe a nested bigQuery table
|
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open
| false
| null |
[] | null | 14
| 2023-09-26T13:29:55
| 2023-12-21T11:45:44
| null |
NONE
| null |
### System Info
Hi - been trying to get langchain sql agent to describe a nested table within bigquery but getting the following error:
parameters: {'param_1': 3}]
(Background on this error at: https://sqlalche.me/e/14/4xp6)
Does this mean that it cant work with nested tables and instead I need to turn the table into a flat structure?
### 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
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
service_account_file = ""
project = ""
dataset = ""
tables = []
sqlalchemy_url = f'bigquery://{project}/{dataset}?credentials_path={service_account_file}'
db = SQLDatabase.from_uri(sqlalchemy_url, include_tables=tables,)
llm = VertexAI(model_name="text-bison-32k")
chat = ChatVertexAI()
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=chat,
toolkit=toolkit,
verbose=True,
top_k=1000,
max_execution_time=100,
early_stopping_method="generate",
agent='conversational-react-description',
memory=memory,
)
agent_executor.run("Can you describe the nested data table?")
### Expected behavior
I expected it to describe the table and its structure
|
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| 1,913,400,664
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PR_kwDOIPDwls5bObdE
| 11,066
|
GCSFileLoader retrieve blob custom metadata and append to document metadata
|
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| null |
[] | null | 2
| 2023-09-26T12:25:54
| 2023-10-18T08:22:34
| 2023-10-17T19:18:00
|
CONTRIBUTOR
| null |
- **Description:** GCSFileLoader retrieve blob's custom metadata and append to document's metadata
- **Issue:** #9975,
- **Tag maintainer:** @baskaryan please review
|
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| 1,913,374,717
|
PR_kwDOIPDwls5bOVwS
| 11,065
|
[ElasticsearchStore] Enable custom Bulk Args
|
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[] | null | 1
| 2023-09-26T12:10:40
| 2023-09-26T19:53:50
| 2023-09-26T19:53:50
|
CONTRIBUTOR
| null |
This enables bulk args like `chunk_size` to be passed down from the ingest methods (from_text, from_documents) to be passed down to the bulk API.
This helps alleviate issues where bulk importing a large amount of documents into Elasticsearch was resulting in a timeout.
Contribution Shoutout
- @elastic
- [x] Updated Integration tests
|
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| 1,913,348,168
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PR_kwDOIPDwls5bOP4d
| 11,064
|
Fix FalkorDB schema retrieval of Labels
|
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| 2023-09-26T11:55:53
| 2023-09-29T12:05:54
| 2023-09-28T23:07:01
|
CONTRIBUTOR
| null |
- **Description:** Fix FalkorDB schema retrieval of Labels,
- **Tag maintainer:** @hwchase17, @baskaryan
Aggregation in RETURN clause it no supported by Cypher anymore, moved out of the RETURN clause.
|
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PR_kwDOIPDwls5bOEIL
| 11,063
|
Add input/output schemas to runnables
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[] | null | 1
| 2023-09-26T11:24:13
| 2023-09-28T10:05:16
| 2023-09-28T10:05:15
|
COLLABORATOR
| null |
This adds `input_schema` and `output_schema` properties to all runnables, which are Pydantic models for the input and output types respectively. These are inferred from the structure of the Runnable as much as possible, the only manual typing needed is
- optionally add type hints to lambdas (which get translated to input/output schemas)
- optionally add type hint to RunnablePassthrough
These schemas can then be used to create JSON Schema descriptions of input and output types, see the tests
- [x] Ensure no InputType and OutputType in our classes use abstract base classes (replace with union of subclasses)
- [x] Implement in BaseChain and LLMChain
- [x] Implement in RunnableBranch
- [x] Implement in RunnableBinding, RunnableMap, RunnablePassthrough, RunnableEach, RunnableRouter
- [x] Implement in LLM, Prompt, Chat Model, Output Parser, Retriever
- [x] Implement in RunnableLambda from function signature
- [x] Implement in Tool
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
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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/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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I_kwDOIPDwls5yCg4S
| 11,062
|
ValueError Regarding Required Metadata Variables When Using RedisVectorStoreRetrieverWithScores
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open
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[] | null | 6
| 2023-09-26T11:05:20
| 2023-10-25T02:18:11
| null |
NONE
| null |
### System Info
LangChain version: **0.0.301**
**Python 3.9.6**
System Version: **macOS 13.5.2 (22G91)**
Kernel Version: **Darwin 22.6.0**
### 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
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I encountered a ValueError when using a custom Redis retriever (RedisVectorStoreRetrieverWithScores) with the SemanticDocumentSearch component in langchain. Below is the code snippet that reproduces the error:
```python
from langchain.vectorstores.redis.base import Redis, RedisVectorStoreRetriever
from langchain.chains import SemanticDocumentSearch
from langchain.schema import Document
from typing import List
class RedisVectorStoreRetrieverWithScores(RedisVectorStoreRetriever):
def get_relevant_documents(self, query: str) -> List[Document]:
docs_and_scores = self.vectorstore.similarity_search_with_score(query, k=self.search_kwargs["k"])
for doc, score in docs_and_scores:
doc.metadata = {**doc.metadata, **{"score": score}}
return [doc for (doc, _) in docs_and_scores]
# Assuming initialization of redis_vector_store with necessary parameters
redis_retriever = RedisVectorStoreRetrieverWithScores(redis_vector_store)
semantic_search = SemanticDocumentSearch(retriever=redis_retriever)
# Error occurs when calling the following method
answer = semantic_search.get_answer("How are you?")
```
Error:
```python
Traceback (most recent call last):
...
ValueError: Document prompt requires documents to have metadata variables: ['source']. Received document with missing metadata: ['source'].
```
Steps to reproduce the behavior:
1. Implement a custom Redis retriever class RedisVectorStoreRetrieverWithScores inheriting from RedisVectorStoreRetriever.
2. Instantiate SemanticDocumentSearch with the custom Redis retriever.
3. Call the get_answer method of the SemanticDocumentSearch instance.
### Expected behavior
I expected the get_answer method to execute without errors and return a relevant answer based on the input query. However, a ValueError is thrown indicating a missing source metadata variable in the Document objects.
I am kindly seeking guidance on how to resolve this issue, and I am very thankful for any assistance or pointers provided. Thank you in advance for your time and help! 😊
|
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BedrockChat has _astream missing
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open
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[] | null | 4
| 2023-09-26T10:58:00
| 2023-10-18T13:02:42
| null |
NONE
| null |
### System Info
After the latest [PR](https://github.com/langchain-ai/langchain/pull/10393/files#diff-9874347f7fa335df661ff4089b0922b3214e08a92e9879610424522f806358f7) it seems that the asynchronous streaming has been missing from libs/langchain/langchain/chat_models/bedrock.py. Why is that? The PR included it, while if you check the file it's not there.
Possible help? @agola11
I'm using LangChain v0.0.301, Python 3.10.
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
N/A, just check the files.
### Expected behavior
Async streaming available.
|
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PR_kwDOIPDwls5bNx_z
| 11,059
|
Callback integration for Trubrics
|
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[] | null | 2
| 2023-09-26T10:35:08
| 2023-09-28T23:20:19
| 2023-09-28T23:20:19
|
CONTRIBUTOR
| null |
Hello LangChain!
After contributing to some examples in the [langsmith-cookbook](https://github.com/langchain-ai/langsmith-cookbook) with @hinthornw, here is a PR that adds a callback handler to use LangChain with [Trubrics](https://github.com/trubrics/trubrics-sdk).
Thanks
|
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I_kwDOIPDwls5yCMJK
| 11,058
|
Refine chain using generator or custom callbacks
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[] | null | 1
| 2023-09-26T10:13:12
| 2023-12-26T16:01:05
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NONE
| null |
### Feature request
Here:
https://github.com/langchain-ai/langchain/blob/a2f7246f0eeafff29b63c90d45ec9e9eb6c21812/libs/langchain/langchain/chains/combine_documents/refine.py#L153C1-L153C1
And here:
https://github.com/langchain-ai/langchain/blob/a2f7246f0eeafff29b63c90d45ec9e9eb6c21812/libs/langchain/langchain/chains/combine_documents/refine.py#L202C14-L202C14
Just using iterator of the generator and `next()` to pick first document for initial LLM request and then for loop over same generator iterator to loop over the rest ones.
The most important is to provide previous response when iterating next Document.
Some callbacks system will also do, which will allow to decide whether to break from the loop and which will accept next document as response from that callback.
### Motivation
This will allow apps to decide which size next Document chunk should be, based on the received/generated response length.
Allowing to develop dynamic chunking based on previous response.
### Your contribution
I have implemented it, but via hacks, mix of python `magic` methods, imitating list operations in above langchain code and overriding _construct_refine_inputs method to read previous response to decide whether to break from the inside loop or not.
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refactor web base loader
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| null |
[] | null | 1
| 2023-09-26T09:40:24
| 2023-09-26T15:13:55
| 2023-09-26T15:11:32
|
COLLABORATOR
| null | null |
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| 1,912,956,333
|
I_kwDOIPDwls5yBWWt
| 11,056
|
Issue: create_csv_agent return the half-unfinished response
|
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| false
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[] | null | 4
| 2023-09-26T08:15:03
| 2023-12-26T16:01:50
| null |
NONE
| null |
### Issue you'd like to raise.
I have one csv file in which 1000 rows and 9 columns are available . When I asked to my agent, provide me the top 10 records, it returns half-unfinished response.
```
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.agents.agent_types import AgentType
from langchain.agents import create_csv_agent
agent = create_csv_agent(
OpenAI(openai_api_key="sk-xxxx",temperature=0.6),
"pinecone_training with 1000 data.csv",
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
print(agent.run("Please provide me the top 10 records."))
```
How can I get the complete finished response?
### Suggestion:
_No response_
|
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| 1,912,920,234
|
I_kwDOIPDwls5yBNiq
| 11,055
|
Github toolkits create_file always return 404 when file does not exists
|
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open
| false
| null |
[] | null | 2
| 2023-09-26T07:54:00
| 2023-12-26T16:01:31
| null |
NONE
| null |
### System Info
Python 3.11.4
langchain 0.0.301
PyGithub 1.59.1
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create a github repository with no README file, create a issue with title `Create README file` and content `Find what the most popular frontend framework is right now and add a short blurb to the readme.md file about how this website will take advantage of it.`.
2. Run official example [Github Simple Agent](https://python.langchain.com/docs/integrations/toolkits/github#example-simple-agent).
```python
agent.run(
"You have the software engineering capabilities of a Google Principle engineer. You are tasked with completing issues on a github repository. Please look at the existing issues and complete them."
)
```
3. Get run logs:
```text
> Entering new AgentExecutor chain...
I need to figure out what issues need to be completed.
Action: Get Issues
Action Input: N/A
Observation: Found 1 issues:
[{'title': 'Create README file', 'number': 1}]
Thought: I need to create the README file
Action: Create File
Action Input: README.md
Observation: Unable to make file due to error:
404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/contents#get-repository-content"}
Thought: I need to create the file in the repository
Action: Create File
Action Input: /test/README.md
Observation: Unable to make file due to error:
404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/contents#get-repository-content"}
Thought: I need to create the file in the correct repository
Action: Create File
Action Input: <repository name>/test/README.md
Observation: Unable to make file due to error:
404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/contents#get-repository-content"}
Thought: I need to make sure I am in the correct repository
Action: Get Issues
Action Input: N/A
Observation: Found 1 issues:
[{'title': 'Create README file', 'number': 1}]
Thought: I now know the correct repository and can create the file
Action: Create File
Action Input: <correct repository name>/test/README.md
Observation: Unable to make file due to error:
404 {"message": "Not Found", "documentation_url": "https://docs.github.com/rest/repos/contents#get-repository-content"}
Thought:
...
```
### Expected behavior
Successfully create file README.md in repository.
|
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| 1,912,873,338
|
I_kwDOIPDwls5yBCF6
| 11,054
|
Use embedding model downloaded from tensorflow hub
|
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open
| false
| null |
[] | null | 1
| 2023-09-26T07:28:32
| 2023-12-26T16:01:11
| null |
NONE
| null |
### Issue you'd like to raise.
'from langchain.embeddings import TensorflowHubEmbeddings' has a model_url parameter to input the embedding model as a url, but I want to use the downloaded embedding model.
### Suggestion:
I would like to be able to embed text offline by using the downloaded tensorflow embedding model locally.
|
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| 1,912,723,653
|
PR_kwDOIPDwls5bMHGx
| 11,053
|
Update notebook deps
|
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| 2023-09-26T05:41:18
| 2023-09-26T05:41:30
| 2023-09-26T05:41:29
|
COLLABORATOR
| null | null |
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| 1,912,715,419
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PR_kwDOIPDwls5bMFP6
| 11,052
|
Add collab link
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| 2023-09-26T05:35:20
| 2023-09-26T05:35:28
| 2023-09-26T05:35:25
|
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| 1,912,683,228
|
PR_kwDOIPDwls5bL-YD
| 11,051
|
fix: short-circuit black and mypy calls when no changes made
|
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| 2023-09-26T04:59:33
| 2023-10-10T18:16:35
| 2023-09-28T23:13:07
|
CONTRIBUTOR
| null |
Both black and mypy expect a list of files or directories as input. As-is the Makefile computes a list files changed relative to the last commit; these are passed to black and mypy in the `format_diff` and `lint_diff` targets. This is done by way of the Makefile variable `PYTHON_FILES`. This is to save time by skipping running mypy and black over the whole source tree.
When no changes have been made, this variable is empty, so the call to black (and mypy) lacks input files. The call exits with error causing the Makefile target to error out with:
```bash
$ make format_diff
poetry run black
Usage: black [OPTIONS] SRC ...
One of 'SRC' or 'code' is required.
make: *** [format_diff] Error 1
```
This is unexpected and undesirable, as the naive caller (that's me! 😄 ) will think something else is wrong. This commit smooths over this by short circuiting when `PYTHON_FILES` is empty.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
|
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PR_kwDOIPDwls5bL9nE
| 11,050
|
Support add_embeddings for opensearch
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| 2023-09-28T23:41:12
|
CONTRIBUTOR
| null |
- **Description:**
- Make running integration test for opensearch easy
- Provide a way to use different text for embedding: refer to #11002 for more of the use case and design decision.
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
|
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PR_kwDOIPDwls5bL4pi
| 11,049
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Make numexpr optional
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| 2023-10-02T18:42:51
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I_kwDOIPDwls5yAGzu
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SQLDatabaseChain.run with Llama 2 caused Chain run OperationalError
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| 2023-09-26T03:55:32
| 2023-11-17T21:15:38
| null |
NONE
| null |
### System Info
My installed versions of LangChain:
```
langchain 0.0.292
langchain-experimental 0.0.20
```
Running in notebook on M1 MBP.
### Who can help?
@hwchase17
### Information
- [x] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [x] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [x] Agents / Agent Executors
- [ ] Tools / Toolkits
- [x] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
My complete code:
```
from langchain.llms import Replicate
from langchain.utilities import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
llama2_13b = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d"
llm = Replicate(
model=llama2_13b,
input={"temperature": 0.01, "max_length": 500, "top_p": 1},
)
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
db_chain.run("How many employees are there?")
```
### Expected behavior
I followed the [SQL Quickstart](https://python.langchain.com/docs/use_cases/qa_structured/sql#quickstart), except using Llama2 on Replicate instead of OpenAI, and got the following error when running `db_chain.run("How many employees are there?")`:
```
db_chain.run("How many employees are there?")
db_chain.run("How many employees are there?")
[chain/start] [1:chain:SQLDatabaseChain] Entering Chain run with input:
{
"query": "How many employees are there?"
}
[chain/start] [1:chain:SQLDatabaseChain > 2:chain:LLMChain] Entering Chain run with input:
{
"input": "How many employees are there?\nSQLQuery:",
"top_k": "5",
"dialect": "sqlite",
"table_info": "\nCREATE TABLE....
Question: How many employees are there?\nSQLQuery:"
]
}
[llm/end] [1:chain:SQLDatabaseChain > 2:chain:LLMChain > 3:llm:Replicate] [3.35s] Exiting LLM run with output:
{
"generations": [
[
{
"text": " Sure! Here's the SQL query to retrieve the number of employees:\n```sql\nSELECT COUNT(*) FROM Employee;\n```\nAnd here's the result:\n```\nCOUNT(*): 3\n```\nThere are 3 employees in the database.",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[chain/end] [1:chain:SQLDatabaseChain > 2:chain:LLMChain] [3.35s] Exiting Chain run with output:
{
"text": " Sure! Here's the SQL query to retrieve the number of employees:\n```sql\nSELECT COUNT(*) FROM Employee;\n```\nAnd here's the result:\n```\nCOUNT(*): 3\n```\nThere are 3 employees in the database."
}
[chain/error] [1:chain:SQLDatabaseChain] [3.37s] Chain run errored with error:
"OperationalError('(sqlite3.OperationalError) near \"Sure\": syntax error')"
```
I think the problem is how to ask Llama2 to return only the SQL query itself without starting with "Sure! Here's the SQL query..." I tried set SYSTEM_PROMPT in llm's input dict, but with no effect.
|
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PR_kwDOIPDwls5bLumv
| 11,047
|
make warnings more modular
|
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| 2023-09-26T03:25:39
| 2023-09-26T03:46:44
| 2023-09-26T03:46:43
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LangServe
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| 2023-09-26T02:08:25
| 2023-09-28T13:20:11
| 2023-09-28T09:52:44
|
COLLABORATOR
| null |
Adds LangServe package
* Integrate Runnables with Fast API creating Server and a RemoteRunnable client
* Support multiple runnables for a given server
* Support sync/async/batch/abatch/stream/astream/astream_log on the client side (using async implementations on server)
* Adds validation using annotations (relying on pydantic under the hood) -- this still has some rough edges -- e.g., open api docs do NOT generate correctly at the moment
* Uses pydantic v1 namespace
Known issues: type translation code doesn't handle a lot of types (e.g., TypedDicts)
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https://github.com/langchain-ai/langchain/issues/11045
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I_kwDOIPDwls5x_wWK
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AnthropicFunctions with Bedrock
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[] | null | 3
| 2023-09-26T01:49:33
| 2023-11-06T01:23:24
| null |
NONE
| null |
### Feature request
Hi, I'm using the Anthropic Claude V2 model through Amazon Bedrock. I'd like to submit a feature request for function calling simialr to `langchain_experimental.llms.anthropic_functions.AnthropicFunctions`
### Motivation
A large amount of people are using Anthropic's models through Bedrock
### Your contribution
Not sure
|
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