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https://api.github.com/repos/langchain-ai/langchain/issues/12917
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| 1,978,229,664 |
I_kwDOIPDwls516WOg
| 12,917 |
ChatTongyi do not support AsyncIteratorCallbackHandler
|
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open
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[] | null | 2 | 2023-11-06T03:48:40 | 2023-11-06T03:57:47 | null |
NONE
| null |
### System Info
python 3.10.0
langchain 0.3.300
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
ChatTongyi and AsyncIteratorCallbackHandler use togther
### Expected behavior
/Users/joeylin/Projects/questionAnswer/venv/lib/python3.10/site-packages/langchain/chat_models/tongyi.py:366: RuntimeWarning: coroutine 'AsyncCallbackManagerForLLMRun.on_llm_new_token' was never awaited
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
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| 1,978,179,255 |
I_kwDOIPDwls516J63
| 12,916 |
DocArrayInMemorySearch | Pydantic 2+ Breaking Changes
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open
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[] | null | 3 | 2023-11-06T02:46:56 | 2024-01-18T04:56:16 | null |
NONE
| null |
### System Info
langchain==0.0.330
Python 3.10.8
### Who can help?
@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
Was using latest release of pydantic (2.4.2).
getting errors to install langchain[docarray]
Above that:
ImportError: cannot import name 'ROOT_KEY' from 'pydantic.main' (...venv\lib\site-packages\pydantic\main.py)
Retried using pydantic 1.10.13. Works fine now. Not sure if worth adding to the error message or building in langchain dependence of pydantic version <2.0
I didn't try all the versions, but I know 2.4.2 breaks.
### Expected behavior
I wasn't able to set my index. Here is where the code broke:
index = VectorstoreIndexCreator(
vectorstore_cls=DocArrayInMemorySearch
).from_loaders([loader])
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| 1,978,114,184 |
PR_kwDOIPDwls5eojls
| 12,914 |
cli template gitignores
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closed
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[] | null | 1 | 2023-11-06T01:40:40 | 2023-11-06T06:34:46 | 2023-11-06T06:34:45 |
COLLABORATOR
| null |
- ap gitignore
- package
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| 1,978,090,823 |
I_kwDOIPDwls5150VH
| 12,913 |
subclassing VectorStoreRetriever causes unjustifiable infinite recursion
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[] | null | 2 | 2023-11-06T01:17:11 | 2023-11-06T01:48:44 | 2023-11-06T01:48:44 |
NONE
| null |
### System Info
Any system.
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [X] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
```python
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import Callbacks
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores.chroma import Chroma
from langchain.schema.vectorstore import VectorStoreRetriever
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chat_models import AzureChatOpenAI
from langchain.docstore.document import Document
import os
class MyRetriever(VectorStoreRetriever):
def get_relevant_documents(self, query: str, *, callbacks: Callbacks = None, tags: List[str] | None = None, metadata: Dict[str, Any] | None = None, run_name: str | None = None, **kwargs: Any) -> List[Document]:
print('Called get_relevant_documents in MyRetriever')
res = super(MyRetriever, self).get_relevant_documents(query, callbacks=callbacks, tags=tags, metadata=metadata, run_name=run_name, **kwargs)
print('Finished get_relevant_documents in MyRetriever')
tmp_res: dict[str, Document] = {}
for item in res:
doc_name = item.page_content.split("\n", maxsplit=1)[0]
if doc_name not in tmp_res:
tmp_res[doc_name] = item
else:
orig_doc = tmp_res[doc_name]
doc_content = "\n".join(item.page_content.split("\n")[1:])
new_doc = Document(page_content=orig_doc.page_content + doc_content, metadata=orig_doc.metadata)
tmp_res[doc_name] = new_doc
return list(tmp_res.values())
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectordb = Chroma.from_documents([Document("blah")], embeddings, persist_directory="./tmp")
ret = MyRetriever(vectorstore=vectordb, tags=vectordb._get_retriever_tags())
ret.get_relevant_documents("blah") # infinite recursion!
```
### Expected behavior
Safely call parent class method then customize its behaviour
|
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PR_kwDOIPDwls5eoQDk
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SQL Query Chain Augmentation
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[] | null | 1 | 2023-11-05T23:20:08 | 2023-11-10T09:42:28 | 2023-11-10T09:40:46 |
CONTRIBUTOR
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create_sql_agent Error: table_names {"'<table name>'"} not found in database.
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[] | null | 4 | 2023-11-05T20:09:44 | 2024-01-13T01:38:57 | null |
NONE
| null |
### System Info
LangChain version: 0.0.330
Windows 11
Python 3.11.3
SQLAlchemy version: 2.0.23
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Steps to reproduce the behavior.
1. Follow the steps mentioned in https://python.langchain.com/docs/integrations/toolkits/sql_database
2. Replace connection string with a PostgreSQL connection string
3. Run the following code, make sure to update the connection string and the database/tables you are querying.
import langchain
from langchain.llms import CTransformers
from langchain.agents import create_sql_agent
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.sql_database import SQLDatabase
from langchain.agents import AgentExecutor
from langchain.agents.agent_types import AgentType
config = {'max_new_tokens': 256, 'repetition_penalty': 1.1, 'temperature': 0, 'context_length': 4096}
llm = CTransformers(model="TheBloke/CodeLlama-7B-Instruct-GGUF",
model_file="codellama-7b-instruct.Q4_K_M.gguf",config=config, verbose=True)
CONNECTION_STRING = 'postgresql://postgresuser:password@localhost:5432/pharmadb'
db = SQLDatabase.from_uri(CONNECTION_STRING)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=llm,
toolkit=toolkit,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
agent_executor.run("Describe the corporation table")
I get the following output in verbose mode..
> Entering new AgentExecutor chain...
Action: sql_db_list_tables
Action Input: ''
Observation: corporation, molecule, product_mat, productpack
Thought: The corporation table is probably one of those tables.
Action: sql_db_schema
Action Input: 'corporation'
Observation: Error: table_names {"'corporation'"} not found in database
Thought: I must have misspelled the name of the table. Let me try again.
Action: sql_db_schema
Action Input: 'corporation'
Observation: Error: table_names {"'corporation'"} not found in database
Thought: I must have misspelled the name of the table. Let me try again.
Action: sql_db_schema
Action Input: 'corporation'
Observation: Error: table_names {"'corporation'"} not found in database
Thought: I must have misspelled the name of the table. Let me try again.
Action: sql_db_schema
Action Input: 'corporation'
Observation: Error: table_names {"'corporation'"} not found in database
Thought: I must have misspelled the name of the table. Let me try again.
Action: sql_db_schema
Action Input: 'corporation'
Observation: Error: table_names {"'corporation'"} not found in database
Thought: I must have misspelled the name of the table. Let me try again.
Action: sql_db_schema
Action Input: 'corporation'
.
.
.
> Finished chain.
'Agent stopped due to iteration limit or time limit.'
### Expected behavior
Expected to correctly identify the table and its schema, run the query to retrieve top 3 records from the table and provide a description of the table.
|
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I_kwDOIPDwls515INz
| 12,910 |
Issue: How to deploy langchain using docker and redis used by langchain
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[] | null | 8 | 2023-11-05T18:40:09 | 2023-11-08T12:13:13 | 2023-11-08T12:13:13 |
CONTRIBUTOR
| null |
### Issue you'd like to raise.
I am using RedisChatMessageHistory from langchain and i am trying to deploy it using docker and i am facing issues in connecting to the redis . can someone help me over here @hwchase17
### Suggestion:
_No response_
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PR_kwDOIPDwls5en3xq
| 12,909 |
fix redis/base.py result.content to result.prompt
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[] | null | 3 | 2023-11-05T18:12:02 | 2023-11-09T22:33:41 | null |
NONE
| null |
- **Description:** fix redis/base.py `result.content` to `result.prompt`
- **Issue:** AttributeError: 'Document' object has no attribute 'content'
- **Dependencies:** None
Code
```python
...
# llm = GooglePalm(...)
# embedding = GooglePalmEmbeddings(...)
llm = OpenAI(...)
embedding = OpenAIEmbeddings(...)
redis_cache = RedisSemanticCache(
redis_url="redis://...:6379",
embedding=embedding,
score_threshold=0.01
)
langchain.llm_cache = redis_cache
...
langchain.llm_cache._cache_dict['...'].similarity_search_with_score(query="hello world")`
```
🛑Debug in LangChain src code:
❌Error(OpenAI and PaLM both) and print debug log:
✅**Success and output result shape after the fix(`result.content` to `result.prompt`)**:
```python
# PaLM
[(Document(page_content='hello world', metadata={'id': 'doc:cache:4d4460ad591fb739063de9681c18b075:d4e9339a552a4b2aaf936da3fcdb2f5f', 'return_val': '[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", "output", "Generation"], "kwargs": {"text": "```\\n#include <stdio.h>\\n\\nint main() {\\n printf(\\"Hello, world!\\\\n\\");\\n return 0;\\n}\\n```"}}]', 'llm_string': "[('_type', 'google_palm'), ('stop', None)]"}),
0.0),
(Document(page_content='reverse the word: hello', metadata={'id': 'doc:cache:4d4460ad591fb739063de9681c18b075:d55bc99d03f946f3a7dc4e1cf363dc23', 'return_val': '[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", "output", "Generation"], "kwargs": {"text": "```\\nolleh\\n```"}}]', 'llm_string': "[('_type', 'google_palm'), ('stop', None)]"}),
0.2481), ...]
```
```python
# OpenAI
[(Document(page_content='hello world', metadata={'id': 'doc:cache:f4d6fdd07c9be488b08c67b57ea4744c:ecc00d18e86a426190b8ed719f41da95', 'return_val': '[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", "output", "Generation"], "kwargs": {"text": "\\n\\nHello world is a common phrase used to express a greeting or acknowledgment of someone or something. It is often used as a friendly greeting when meeting someone for the first time.", "generation_info": {"finish_reason": "stop", "logprobs": null}}}]', 'llm_string': "[('_type', 'openai'), ('frequency_penalty', 0), ('logit_bias', {}), ('max_tokens', 256), ('model_name', 'text-davinci-003'), ('n', 1), ('presence_penalty', 0), ('request_timeout', None), ('stop', None), ('temperature', 0.7), ('top_p', 1)]"}),
0.0),
(Document(page_content='reverse the word: hello', metadata={'id': 'doc:cache:f4d6fdd07c9be488b08c67b57ea4744c:d8314c01a7454f6ebf02253f238269c8', 'return_val': '[{"lc": 1, "type": "constructor", "id": ["langchain", "schema", "output", "Generation"], "kwargs": {"text": "\\n\\nolleh", "generation_info": {"finish_reason": "stop", "logprobs": null}}}]', 'llm_string': "[('_type', 'openai'), ('frequency_penalty', 0), ('logit_bias', {}), ('max_tokens', 256), ('model_name', 'text-davinci-003'), ('n', 1), ('presence_penalty', 0), ('request_timeout', None), ('stop', None), ('temperature', 0.7), ('top_p', 1)]"}),
0.1373),...]
```
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PR_kwDOIPDwls5enzyK
| 12,908 |
docs: update hf pipeline docs
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[] | null | 1 | 2023-11-05T17:15:27 | 2023-11-06T15:52:36 | 2023-11-06T15:51:32 |
CONTRIBUTOR
| null |
- **Description:** Noticed that the Hugging Face Pipeline documentation was a bit out of date.
Updated with information about passing in a pipeline directly (consistent with docstring) and a recent contribution of mine on adding support for multi-gpu specifications with Accelerate in 21eeba075c05714f185e5541f25228f7b555f606
<!-- 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/langchain-ai/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_kwDOIPDwls5enyPD
| 12,907 |
docs: clean up vestigial markdown
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[] | null | 1 | 2023-11-05T16:54:30 | 2023-11-06T15:51:56 | 2023-11-06T15:51:56 |
CONTRIBUTOR
| null |
- **Description:** Remove text "LangChain currently does not support" which appears to be vestigial leftovers from a previous change.
- **Issue:** N/A
- **Dependencies:** N/A
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PR_kwDOIPDwls5enyC7
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Remove useless line of code from sql.ipynb
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[] | null | 1 | 2023-11-05T16:51:49 | 2023-11-06T15:59:12 | 2023-11-06T15:59:12 |
CONTRIBUTOR
| null |
This PR remove a single line of code from a notebook of the documentation. This line used to define a variable, which is never used in the code.
For further context, for reviewers, here is the online documentation: https://python.langchain.com/docs/use_cases/qa_structured/sql#case-3-sql-agents
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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- **Issue:** the issue # it fixes (if applicable),
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- **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.
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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|
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| 1,977,817,267 |
PR_kwDOIPDwls5enn5W
| 12,905 |
🎛️ improve interface with __call__ and __await__
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[] | null | 1 | 2023-11-05T14:40:28 | 2023-11-17T14:52:17 | 2023-11-17T14:40:32 |
CONTRIBUTOR
| null |
Improves the interface of Runnables by making it universally accessible via sync/async.
Just __call__ or __await__ it.
@nfcampos we talked about this in Slack :)
|
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I_kwDOIPDwls514rRY
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DOC: Links at the bottom of Plan and Execute Agents blog do not work
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[] | null | 2 | 2023-11-05T13:34:23 | 2023-11-05T13:40:20 | null |
NONE
| null |
### Issue with current documentation:
https://blog.langchain.dev/plan-and-execute-agents/
Links at the end of the Conclusion section do not work.
[here (Python)](https://python.langchain.com/docs/modules/agents/agent_types/plan_and_execute?ref=blog.langchain.dev
) and [here (JS)](https://js.langchain.com/docs/modules/agents/agents/examples/plan_and_execute_agent?ref=blog.langchain.dev) are broken.
### Idea or request for content:
_No response_
|
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Issue: <[chain/error] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 9:chain:LLMChain] [2.01s] Chain run errored with error: "IndexError('list index out of range')">
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[] | null | 3 | 2023-11-05T12:19:52 | 2023-12-09T16:44:36 | null |
NONE
| null |
### Issue you'd like to raise.
``I am using Google Palm,Faiss,HF Instruct Embeddings.When ever I am Quering with RetrievalQAWithSourcesChain.
I am getting
```
[chain/error] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 9:chain:LLMChain] [2.01s] Chain run errored with error:
"IndexError('list index out of range')"
[chain/error] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain] [6.45s] Chain run errored with error:
"IndexError('list index out of range')"
[chain/error] [1:chain:RetrievalQAWithSourcesChain] [7.13s] Chain run errored with error:
"IndexError('list index out of range')"
```
Here is my whole code
``````
import` os
import streamlit as st
import pickle
import time
import langchain
from langchain.llms import GooglePalm
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import SeleniumURLLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
urls = ["https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS",
"https://www.livemint.com/companies/news/tata-motorss-ev-subsidiary-to-sign-mou-with-jlr-to-strengthen-ev-business-tata-motors-to-pay-royalty-fee-to-jlr-11698925980903.html"
]
loader=SeleniumURLLoader(urls=urls)
data=loader.load()
llm=GooglePalm(google_api_key="", temperature=0.9,max_output_tokens=500)
r_splitter = RecursiveCharacterTextSplitter(
separators=["\n\n", "\n", " ","\t\t"],
chunk_size=400,
chunk_overlap=80
)
docs = r_splitter.split_documents(data)
embeddings = HuggingFaceInstructEmbeddings(
query_instruction="Represent the query for retrieval: "
)
vector_index = FAISS.from_documents(docs, embeddings)
chain = RetrievalQAWithSourcesChain.from_llm(llm=llm, retriever=vector_index.as_retriever())
chain
langchain.debug=True
query="summerise the text"
chain({"question":query},return_only_outputs=True)
```
```
OUTPUT:
`[chain/start] [1:chain:RetrievalQAWithSourcesChain] Entering Chain run with input:
{
"question": "summerise the text"
}
[chain/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain] Entering Chain run with input:
[inputs]
[chain/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain] Entering Chain run with input:
{
"input_list": [
{
"context": "View more \n \n \n Posted by : kamal20",
"question": "summerise the text"
},
{
"context": "View more \n \n \n Posted by : kamal20",
"question": "summerise the text"
},
{
"context": "- - \n - - \n - - \n - - \n - -",
"question": "summerise the text"
},
{
"context": "- - \n - - \n - - \n - - \n - -",
"question": "summerise the text"
}
]
}
[llm/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 5:llm:GooglePalm] Entering LLM run with input:
{
"prompts": [
"Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nView more \n \n \n Posted by : kamal20\nQuestion: summerise the text\nRelevant text, if any:"
]
}
[llm/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 6:llm:GooglePalm] Entering LLM run with input:
{
"prompts": [
"Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\nView more \n \n \n Posted by : kamal20\nQuestion: summerise the text\nRelevant text, if any:"
]
}
[llm/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 7:llm:GooglePalm] Entering LLM run with input:
{
"prompts": [
"Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\n- - \n - - \n - - \n - - \n - -\nQuestion: summerise the text\nRelevant text, if any:"
]
}
[llm/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 8:llm:GooglePalm] Entering LLM run with input:
{
"prompts": [
"Use the following portion of a long document to see if any of the text is relevant to answer the question. \nReturn any relevant text verbatim.\n- - \n - - \n - - \n - - \n - -\nQuestion: summerise the text\nRelevant text, if any:"
]
}
[llm/end] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 5:llm:GooglePalm] [4.40s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "This article discusses the concept of summarizing documents and provides a detailed overview of the steps involved in the process. It also provides examples of summaries and discusses the different types of summaries that can be created.",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[llm/end] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 6:llm:GooglePalm] [4.40s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "The main points are:",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[llm/end] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 7:llm:GooglePalm] [4.40s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "- - \n\n- - ",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[llm/end] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain > 8:llm:GooglePalm] [4.40s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "- - ",
"generation_info": null
}
]
],
"llm_output": null,
"run": null
}
[chain/end] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 4:chain:LLMChain] [4.41s] Exiting Chain run with output:
{
"outputs": [
{
"text": "This article discusses the concept of summarizing documents and provides a detailed overview of the steps involved in the process. It also provides examples of summaries and discusses the different types of summaries that can be created."
},
{
"text": "The main points are:"
},
{
"text": "- - \n\n- - "
},
{
"text": "- - "
}
]
}
[chain/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 9:chain:LLMChain] Entering Chain run with input:
{
"question": "summerise the text",
"summaries": "Content: This article discusses the concept of summarizing documents and provides a detailed overview of the steps involved in the process. It also provides examples of summaries and discusses the different types of summaries that can be created.\nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n\nContent](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n/nContent): The main points are:\nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n\nContent](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n/nContent): - - \n\n- - \nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n\nContent](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n/nContent): - - \nSource: https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS"
}
[llm/start] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 9:chain:LLMChain > 10:llm:GooglePalm] Entering LLM run with input:
{
"prompts": [
"Given the following extracted parts of a long document and a question, create a final answer with references (\"SOURCES\"). \nIf you don't know the answer, just say that you don't know. Don't try to make up an answer.\nALWAYS return a \"SOURCES\" part in your answer.\n\nQUESTION: Which state/country's law governs the interpretation of the contract?\n=========\nContent: This Agreement is governed by English law and the parties submit to the exclusive jurisdiction of the English courts in relation to any dispute (contractual or non-contractual) concerning this Agreement save that either party may apply to any court for an injunction or other relief to protect its Intellectual Property Rights.\nSource: 28-pl\nContent: No Waiver. Failure or delay in exercising any right or remedy under this Agreement shall not constitute a waiver of such (or any other) right or remedy.\n\n11.7 Severability. The invalidity, illegality or unenforceability of any term (or part of a term) of this Agreement shall not affect the continuation in force of the remainder of the term (if any) and this Agreement.\n\n11.8 No Agency. Except as expressly stated otherwise, nothing in this Agreement shall create an agency, partnership or joint venture of any kind between the parties.\n\n11.9 No Third-Party Beneficiaries.\nSource: 30-pl\nContent: (b) if Google believes, in good faith, that the Distributor has violated or caused Google to violate any Anti-Bribery Laws (as defined in Clause 8.5) or that such a violation is reasonably likely to occur,\nSource: 4-pl\n=========\nFINAL ANSWER: This Agreement is governed by English law.\nSOURCES: 28-pl\n\nQUESTION: What did the president say about Michael Jackson?\n=========\nContent: Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n\nLast year COVID-19 kept us apart. This year we are finally together again. \n\nTonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n\nWith a duty to one another to the American people to the Constitution. \n\nAnd with an unwavering resolve that freedom will always triumph over tyranny. \n\nSix days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways. But he badly miscalculated. \n\nHe thought he could roll into Ukraine and the world would roll over. Instead he met a wall of strength he never imagined. \n\nHe met the Ukrainian people. \n\nFrom President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n\nGroups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland.\nSource: 0-pl\nContent: And we won’t stop. \n\nWe have lost so much to COVID-19. Time with one another. And worst of all, so much loss of life. \n\nLet’s use this moment to reset. Let’s stop looking at COVID-19 as a partisan dividing line and see it for what it is: A God-awful disease. \n\nLet’s stop seeing each other as enemies, and start seeing each other for who we really are: Fellow Americans. \n\nWe can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n\nI recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n\nThey were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n\nOfficer Mora was 27 years old. \n\nOfficer Rivera was 22. \n\nBoth Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n\nI spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves.\nSource: 24-pl\nContent: And a proud Ukrainian people, who have known 30 years of independence, have repeatedly shown that they will not tolerate anyone who tries to take their country backwards. \n\nTo all Americans, I will be honest with you, as I’ve always promised. A Russian dictator, invading a foreign country, has costs around the world. \n\nAnd I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n\nTonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. \n\nAmerica will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. \n\nThese steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n\nBut I want you to know that we are going to be okay.\nSource: 5-pl\nContent: More support for patients and families. \n\nTo get there, I call on Congress to fund ARPA-H, the Advanced Research Projects Agency for Health. \n\nIt’s based on DARPA—the Defense Department project that led to the Internet, GPS, and so much more. \n\nARPA-H will have a singular purpose—to drive breakthroughs in cancer, Alzheimer’s, diabetes, and more. \n\nA unity agenda for the nation. \n\nWe can do this. \n\nMy fellow Americans—tonight , we have gathered in a sacred space—the citadel of our democracy. \n\nIn this Capitol, generation after generation, Americans have debated great questions amid great strife, and have done great things. \n\nWe have fought for freedom, expanded liberty, defeated totalitarianism and terror. \n\nAnd built the strongest, freest, and most prosperous nation the world has ever known. \n\nNow is the hour. \n\nOur moment of responsibility. \n\nOur test of resolve and conscience, of history itself. \n\nIt is in this moment that our character is formed. Our purpose is found. Our future is forged. \n\nWell I know this nation.\nSource: 34-pl\n=========\nFINAL ANSWER: The president did not mention Michael Jackson.\nSOURCES:\n\nQUESTION: summerise the text\n=========\nContent: This article discusses the concept of summarizing documents and provides a detailed overview of the steps involved in the process. It also provides examples of summaries and discusses the different types of summaries that can be created.\nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n\nContent](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n/nContent): The main points are:\nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n\nContent](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n/nContent): - - \n\n- - \nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n\nContent](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n/nContent): - - \nSource: [https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS\n=========\nFINAL](https://www.moneycontrol.com/india/stockpricequote/ironsteel/tatasteel/TIS/n=========/nFINAL) ANSWER:"
]
}
[llm/end] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 9:chain:LLMChain > 10:llm:GooglePalm] [2.01s] Exiting LLM run with output:
{
"generations": [
[]
],
"llm_output": null,
"run": null
}
[chain/error] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain > 9:chain:LLMChain] [2.01s] Chain run errored with error:
"IndexError('list index out of range')"
[chain/error] [1:chain:RetrievalQAWithSourcesChain > 3:chain:MapReduceDocumentsChain] [6.45s] Chain run errored with error:
"IndexError('list index out of range')"
[chain/error] [1:chain:RetrievalQAWithSourcesChain] [7.13s] Chain run errored with error:
"IndexError('list index out of range')"`
### Suggestion:
_No response_
|
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| 1,977,689,238 |
I_kwDOIPDwls514SSW
| 12,900 |
Finished chain - None, when I run an csv_agent on a csv file.
|
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open
| false | null |
[] | null | 2 | 2023-11-05T09:30:48 | 2023-11-05T09:40:51 | null |
NONE
| null |
### Issue you'd like to raise.
My openai llm see to run fine but some how not getting the answer in Finished chain.
Model used
```
def llm_function(csv_file_name):
agent = create_csv_agent(
ChatOpenAI(temperature=0, model="gpt-3.5-turbo-0613", request_timeout=120),
csv_file_name,
verbose=True,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
)
# Pass a query to the chain
query = QUESTION
query = query + " using tool python_repl_ast"
#response = agent.run(query)
try:
response = agent.run(query)
except ValueError as e:
response = str(e)
if not response.startswith("Could not parse LLM output: `"):
raise e
response = response.removeprefix("Could not parse LLM output: `").removesuffix("`")
return response
```
Results
```
> Entering new AgentExecutor chain...
Thought: To find the least expensive product, I need to sort the dataframe by the "Price" column in ascending order and then select the first row.
Action: python_repl_ast
Action Input: df.sort_values("Price").head(1)
Observation: Product Name Price Price per Count Bought Last Month
296 MAGNESIUM PHOSPHORICUM 6C 30 ML SBL 90 Price_count not found Data not available
Thought:The least expensive product is "MAGNESIUM PHOSPHORICUM 6C 30 ML SBL" with a price of 90.
Final Answer: "MAGNESIUM PHOSPHORICUM 6C 30 ML SBL"
> Finished chain.
None
```
### Suggestion:
_No response_
|
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I_kwDOIPDwls514VQG
| 12,901 |
Langchain connects Neo4j v5.9 error Could not use APOC procedures
|
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| false | null |
[] | null | 7 | 2023-11-05T09:13:20 | 2023-12-24T23:27:46 | 2023-11-05T09:29:20 |
NONE
| null |
### System Info
I have successfully installed the apoc plugin by pressing the neo4j document, and run ` return apoc.version() ` on the neo4j client to return successfully.
However, when Neo4jGraph was used in Langchain connection, errors were still reported.
```
ValueError: Could not use APOC procedures. Please ensure the APOC plugin is installed in Neo4j and that 'apoc.meta.data()' is allowed in Neo4j configuration
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.graphs import Neo4jGraph
graph = Neo4jGraph(
'bolt://localhost:7687',
'neo4j',
'chenhuabc'
)
print(graph)
# ValueError: Could not use APOC procedures. Please ensure the APOC plugin is installed in Neo4j and that 'apoc.meta.data()' is allowed in Neo4j configuration
### Expected behavior
I have successfully installed the apoc plugin by pressing the neo4j document, and run ` return apoc.version() ` on the neo4j client to return successfully.
However, when Neo4jGraph was used in Langchain connection, errors were still reported.
```
ValueError: Could not use APOC procedures. Please ensure the APOC plugin is installed in Neo4j and that 'apoc.meta.data()' is allowed in Neo4j configuration
```
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| 1,977,674,946 |
I_kwDOIPDwls514OzC
| 12,899 |
Encounter KeyError: 'user_agent' while using CohereRerank()
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| false | null |
[] | null | 5 | 2023-11-05T07:40:49 | 2023-11-29T15:49:34 | null |
NONE
| null |
### System Info
langchain==0.0.330
Python 3.10.12
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
#
compressor = CohereRerank()
#
Error Encountered
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-29-be190c4da94f>](https://localhost:8080/#) in <cell line: 5>()
3
4 #
----> 5 compressor = CohereRerank()
6 #
7 compression_retriever = ContextualCompressionRetriever(
2 frames
/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()
/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.validate_model()
[/usr/local/lib/python3.10/dist-packages/langchain/retrievers/document_compressors/cohere_rerank.py](https://localhost:8080/#) in validate_environment(cls, values)
53 values, "cohere_api_key", "COHERE_API_KEY"
54 )
---> 55 client_name = values["user_agent"]
56 values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
57 return values
KeyError: 'user_agent'
### Expected behavior
The below error should not have been encountered as the COHERE API Key has alreday been set and cohere.Client() is workin with the api key provided
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
[<ipython-input-29-be190c4da94f>](https://localhost:8080/#) in <cell line: 5>()
3
4 #
----> 5 compressor = CohereRerank()
6 #
7 compression_retriever = ContextualCompressionRetriever(
2 frames
/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()
/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.validate_model()
[/usr/local/lib/python3.10/dist-packages/langchain/retrievers/document_compressors/cohere_rerank.py](https://localhost:8080/#) in validate_environment(cls, values)
53 values, "cohere_api_key", "COHERE_API_KEY"
54 )
---> 55 client_name = values["user_agent"]
56 values["client"] = cohere.Client(cohere_api_key, client_name=client_name)
57 return values
KeyError: 'user_agent'
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PR_kwDOIPDwls5em-Qg
| 12,898 |
Harrison/docs smith serve
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[] | null | 1 | 2023-11-05T02:45:38 | 2023-11-06T15:07:27 | 2023-11-06T15:07:26 |
COLLABORATOR
| null | null |
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| 1,977,595,806 |
PR_kwDOIPDwls5em8-2
| 12,897 |
add ingest for mongo
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[] | null | 1 | 2023-11-05T02:13:56 | 2023-11-07T03:28:23 | 2023-11-07T03:28:22 |
COLLABORATOR
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| 1,977,594,465 |
PR_kwDOIPDwls5em8uB
| 12,896 |
Add SecretStr for Symbl.ai Nebula API
|
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CONTRIBUTOR
| null |
Description: This PR masks API key secrets for the Nebula model from Symbl.ai
Issue: #12165
Maintainer: @eyurtsev
|
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PR_kwDOIPDwls5em3JX
| 12,895 |
Update ollama.py
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[] | null | 2 | 2023-11-05T00:11:46 | 2023-11-15T22:49:01 | 2023-11-13T07:43:59 |
CONTRIBUTOR
| null |
duplicate option removed
**Description:** An issue fix, http stop option duplicate removed.
**Issue:** the issue #12892 fix
**Dependencies:** no
**Tag maintainer:** @eyurtsev
|
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| 1,977,527,950 |
I_kwDOIPDwls513q6O
| 12,894 |
LLMChain does not stream
|
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[] | null | 4 | 2023-11-04T22:00:53 | 2024-01-17T19:32:54 | null |
CONTRIBUTOR
| null |
### System Info
Platform: macOS Sonoma 14.0
Python Version: 3.11
LangChain Version: 0.0.330
### Who can help?
@agola11
### Information
- [ ] The official example notebooks/scripts
- [x] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
chain = LLMChain(llm=llm, prompt=prompt)
for chunk in chain.stream({ "product": "colorful socks" })
print(f"Chunk: {chunk}")
```
### Expected behavior
One expects to receive chunks when streaming, but because the `stream` method is not implemented in the `LLMChain class`, it falls back to the `stream` method in the base `Chain` class. This results in a `chunk` variable containing the full response.
This can be fixed easily by something like this.
```python
from langchain.chains import LLMChain
class MyChain(LLMChain):
def stream(
self,
input: Input,
config: Optional[RunnableConfig] = None,
run_manager: Optional[CallbackManagerForChainRun] = None,
**kwargs: Optional[Any],
) -> Iterator[Output]:
prompts, stop = self.prep_prompts([input], run_manager=run_manager)
yield from self.llm.stream(input=prompts[0], config=config, **kwargs)
```
```python
prompt = PromptTemplate.from_template("What is a good name for a company that makes {product}?")
chain = MyChain(llm=llm, prompt=prompt)
for chunk in chain.stream({ "product": "colorful socks" })
print(f"Chunk: {chunk}") ✅
```
If you agree, I would like to create PR which will fix that.
|
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PR_kwDOIPDwls5emvw0
| 12,893 |
Add "messages" key to output of AgentExecutor.stream()
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[] | null | 2 | 2023-11-04T21:59:05 | 2023-11-21T10:32:51 | 2023-11-21T10:32:50 |
COLLABORATOR
| null |
```
---- chunk 1
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]}
---- chunk 2
{'messages': [FunctionMessage(content="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”", name='Search')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”")]}
---- chunk 3
{'actions': [AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]}
---- chunk 4
{'messages': [FunctionMessage(content='25 years', name='Search')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]), observation='25 years')]}
---- chunk 5
{'actions': [AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]}
---- chunk 6
{'messages': [FunctionMessage(content='Answer: 3.991298452658078', name='Calculator')],
'steps': [AgentStep(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
---- chunk 7
{'messages': [AIMessage(content="Leonardo DiCaprio's current girlfriend is the Italian model Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 power is approximately 3.99.")],
'output': "Leonardo DiCaprio's current girlfriend is the Italian model "
'Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 '
'power is approximately 3.99.'}
---- final
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]),
AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]),
AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'messages': [AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}}),
FunctionMessage(content="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”", name='Search'),
AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}}),
FunctionMessage(content='25 years', name='Search'),
AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}}),
FunctionMessage(content='Answer: 3.991298452658078', name='Calculator'),
AIMessage(content="Leonardo DiCaprio's current girlfriend is the Italian model Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 power is approximately 3.99.")],
'output': "Leonardo DiCaprio's current girlfriend is the Italian model "
'Vittoria Ceretti, who is 25 years old. Her age raised to the 0.43 '
'power is approximately 3.99.',
'steps': [AgentStep(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="According to Us, the 48-year-old actor is now “exclusively” dating Italian model Vittoria Ceretti. A source told Us that DiCaprio is “completely smitten” with Ceretti, and their relationship is “going so well that Leo's actually being exclusive.”"),
AgentStep(action=AgentActionMessageLog(tool='Search', tool_input='Vittoria Ceretti age', log='\nInvoking: `Search` with `Vittoria Ceretti age`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti age"\n}'}})]), observation='25 years'),
AgentStep(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
```
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| 1,977,518,229 |
I_kwDOIPDwls513oiV
| 12,892 |
It seems that stop does not work with ollama models.
|
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open
| false | null |
[] | null | 6 | 2023-11-04T21:30:07 | 2023-11-05T10:07:14 | null |
CONTRIBUTOR
| null |
### System Info
pip freeze
attrs==19.3.0
Automat==0.8.0
blinker==1.4
certifi==2019.11.28
chardet==3.0.4
Click==7.0
cloud-init==23.3.1
colorama==0.4.3
command-not-found==0.3
configobj==5.0.6
constantly==15.1.0
cryptography==2.8
dbus-python==1.2.16
diskcache==5.6.3
distro==1.4.0
distro-info==0.23+ubuntu1.1
entrypoints==0.3
httplib2==0.14.0
hyperlink==19.0.0
idna==2.8
importlib-metadata==1.5.0
incremental==16.10.1
Jinja2==2.10.1
jsonpatch==1.22
jsonpointer==2.0
jsonschema==3.2.0
keyring==18.0.1
language-selector==0.1
launchpadlib==1.10.13
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
llama-cpp-python==0.2.11
MarkupSafe==1.1.0
more-itertools==4.2.0
netifaces==0.10.4
numpy==1.24.4
oauthlib==3.1.0
pexpect==4.6.0
pyasn1==0.4.2
pyasn1-modules==0.2.1
PyGObject==3.36.0
PyHamcrest==1.9.0
PyJWT==1.7.1
pymacaroons==0.13.0
PyNaCl==1.3.0
pyOpenSSL==19.0.0
pyrsistent==0.15.5
pyserial==3.4
python-apt==2.0.1+ubuntu0.20.4.1
python-debian==0.1.36+ubuntu1.1
PyYAML==5.3.1
requests==2.22.0
requests-unixsocket==0.2.0
SecretStorage==2.3.1
service-identity==18.1.0
simplejson==3.16.0
six==1.14.0
sos==4.5.6
ssh-import-id==5.10
systemd-python==234
Twisted==18.9.0
typing-extensions==4.8.0
ubuntu-advantage-tools==8001
ufw==0.36
unattended-upgrades==0.1
urllib3==1.25.8
wadllib==1.3.3
zipp==1.0.0
zope.interface==4.7.1
### Who can help?
I'm trying to run an agent based on local `ollama mistral:7b-instruct` and stop is not working. Is it a bug or I configured wrong? Thanks in advance!
```
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain.llms import OpenAI
from langchain.utilities import SerpAPIWrapper
from langchain.chains import LLMChain
from typing import List, Union
from langchain.schema import AgentAction, AgentFinish, OutputParserException
import re
from langchain.llms import Ollama
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout_final_only import FinalStreamingStdOutCallbackHandler
from langchain.agents import tool
@tool
def get_word_length(word: str) -> int:
"""Returns the length of a word."""
return len(word.strip().split()[0])
tools = [get_word_length]
# Set up the base template
template = """Answer the following questions as best you can. You have access to the following tools:
{tools}
Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_names}]
Action Input: the input to the action
Observation: the result of the action
... (this Thought/Action/Action Input/Observation can repeat N times)
Thought: I now know the final answer
Final Answer: the final answer to the original input question
Begin! If you can use a tool, use it before the final answer. Do not make up an observation before calling an action!
Question: {input}
{agent_scratchpad}"""
# Set up a prompt template
class CustomPromptTemplate(StringPromptTemplate):
# The template to use
template: str
# The list of tools available
tools: List[Tool]
def format(self, **kwargs) -> str:
# Get the intermediate steps (AgentAction, Observation tuples)
# Format them in a particular way
intermediate_steps = kwargs.pop("intermediate_steps")
thoughts = ""
for action, observation in intermediate_steps:
thoughts += action.log
thoughts += f"\nObservation: {observation}\nThought: "
# Set the agent_scratchpad variable to that value
kwargs["agent_scratchpad"] = thoughts
# Create a tools variable from the list of tools provided
kwargs["tools"] = "\n".join([f"{tool.name}: {tool.description}" for tool in self.tools])
# Create a list of tool names for the tools provided
kwargs["tool_names"] = ", ".join([tool.name for tool in self.tools])
return self.template.format(**kwargs)
prompt = CustomPromptTemplate(
template=template,
tools=tools,
# This omits the `agent_scratchpad`, `tools`, and `tool_names` variables because those are generated dynamically
# This includes the `intermediate_steps` variable because that is needed
input_variables=["input", "intermediate_steps"]
)
class CustomOutputParser(AgentOutputParser):
def parse(self, llm_output: str) -> Union[AgentAction, AgentFinish]:
# Check if agent should finish
if "Final Answer:" in llm_output:
return AgentFinish(
# Return values is generally always a dictionary with a single `output` key
# It is not recommended to try anything else at the moment :)
return_values={"output": llm_output.split("Final Answer:")[-1].strip()},
log=llm_output,
)
# Parse out the action and action input
regex = r"Action\s*\d*\s*:(.*?)\nAction\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
match = re.search(regex, llm_output, re.DOTALL)
if not match:
raise OutputParserException(f"Could not parse LLM output: `{llm_output}`")
action = match.group(1).strip()
action_input = match.group(2)
# Return the action and action input
return AgentAction(tool=action, tool_input=action_input.strip(" ").strip('"'), log=llm_output)
output_parser = CustomOutputParser()
ollama = Ollama(model="mistral:7b-instruct", callbacks=[FinalStreamingStdOutCallbackHandler()])
# LLM chain consisting of the LLM and a prompt
llm_chain = LLMChain(llm=ollama, prompt=prompt)
tool_names = [tool.name for tool in tools]
agent = LLMSingleActionAgent(
llm_chain=llm_chain,
output_parser=output_parser,
stop=["Observation:"],
allowed_tools=tool_names
)
agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
agent_executor.run("How many letters in the word astronomia?")
```
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [X] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Run the code above
### Expected behavior
Expected to stop on Observation
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| 1,977,501,097 |
I_kwDOIPDwls513kWp
| 12,891 |
DOC: Typo under docs for using chat models for ReAct agents
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| false | null |
[] | null | 1 | 2023-11-04T20:46:53 | 2023-11-04T20:47:16 | null |
NONE
| null |
### Issue with current documentation:
Page link: https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models
Under "Using chat models", second paragraph:
```The main difference here is a different prompt. We will use JSON to encode the agent's actions (chat models are a bit tougher to steet, so using JSON helps to enforce the output format).```
_steet_ should be replaced with _steer_.
### Idea or request for content:
_No response_
|
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PR_kwDOIPDwls5emlaa
| 12,890 |
Fix configurable for non-hashable options
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[] | null | 1 | 2023-11-04T19:21:11 | 2023-11-05T09:12:43 | 2023-11-05T09:12:42 |
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/langchain-ai/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_kwDOIPDwls5ema1O
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mongo parent document retrieval
|
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[] | null | 1 | 2023-11-04T16:35:13 | 2023-11-04T17:16:03 | 2023-11-04T17:16:02 |
COLLABORATOR
| null | null |
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| 1,977,404,351 |
PR_kwDOIPDwls5emXlg
| 12,885 |
Update tool description on uploaded and removed file
|
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[] | null | 4 | 2023-11-04T15:46:17 | 2023-11-13T14:36:42 | null |
CONTRIBUTOR
| null |
For the llm to work properly with the tool, it needs context of the current files. This seems little bit hackish, so if you have better approach, let me know!
<!-- 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/langchain-ai/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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| 1,977,369,918 |
PR_kwDOIPDwls5emQk-
| 12,884 |
fixed from_embeddings documentation
|
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NONE
| null |
- **Description:** Updated FAISS documentation that throws an error,
|
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| 1,977,348,240 |
I_kwDOIPDwls512_CQ
| 12,883 |
Langchain Structured_Chat_Zero_Shot_Description agent use no tools
|
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[] | null | 2 | 2023-11-04T13:49:20 | 2023-11-04T13:58:40 | null |
NONE
| null |
### System Info
Name: langchain
Version: 0.0.308
Macos ventura 13.2.1
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have two Custom tools :
CustomJiraTicketWriting which use FewShotChatMessagePromptTemplate and StructuredOutputParser.from_response_schemas in order to produce dictionary output with a compatible & developed JSON representing a Jira ticket.
CustomJiraTicketPOST which accept 4 inputs : ticket / email / url / token in order to POST the ticket on Jira.
Both of these function works great separately, but when I use an Structured_Chat_Zero_Shot_Description agent to create a ReACT decision, it appears that the Agent refuse to use any of the tools.
I wonder if i do something wrong or if the Structured_Chat_Zero_Shot_Description agent is bugged ?
```
import os
import re
import json
import openai
import random
import langchain
import demjson3
import requests
from langchain.chat_models import ChatOpenAI
from requests.auth import HTTPBasicAuth
from openai import ChatCompletion
from typing import Optional, Type, List, Dict, Union
from langchain.llms import OpenAI,GPT4All
from pydantic import BaseModel, Field
from langchain.agents import (AgentType,
initialize_agent,
AgentOutputParser,
LLMSingleActionAgent,
AgentExecutor,
load_tools)
from langchain.schema import AgentAction, AgentFinish
from langchain.cache import InMemoryCache
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.prompts.few_shot_with_templates import FewShotPromptWithTemplates
from langchain.prompts.few_shot import FewShotPromptTemplate
from langchain.pydantic_v1 import BaseModel, Field, validator
from langchain.chains import LLMChain, SequentialChain, SimpleSequentialChain, TransformChain
from langchain.output_parsers import (OutputFixingParser,
RetryWithErrorOutputParser,
PydanticOutputParser,
CombiningOutputParser,
ResponseSchema,
StructuredOutputParser)
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.prompts import (ChatPromptTemplate,
PromptTemplate,
SystemMessagePromptTemplate,
AIMessagePromptTemplate,
HumanMessagePromptTemplate,
FewShotChatMessagePromptTemplate,
StringPromptTemplate,
)
from langchain.schema import (AIMessage,
HumanMessage,
SystemMessage
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
email = '[email protected]'
url = "https://XXXXXXXXXX.atlassian.net/rest/api/3/issue"
debug_mode = True
langchain.debug = True
langchain.llm_cache = InMemoryCache()
os.environ["JIRA_API_TOKEN"]= 'XXXXXXXXXX'
api_token_jira = os.environ["JIRA_API_TOKEN"]
os.environ["JIRA_USERNAME"]= 'XXXXXXXXXX'
os.environ["JIRA_INSTANCE_URL"] = "XXXXXXXXXX"
site_jira = os.environ["JIRA_INSTANCE_URL"]
os.environ['OPENAI_API_KEY']='XXXXXXXXXX'
openai_api_key = os.getenv('OPENAI_API_KEY')
api_key = os.getenv('OPENAI_API_KEY')
model_name = 'gpt-3.5-turbo'
temperature = 0.0
model_llm = OpenAI(model_name=model_name,
temperature=temperature,
max_tokens=3500)
model_chat = ChatOpenAI(
temperature=temperature,
max_tokens=3100,
model_name=model_name)
turbo_llm = ChatOpenAI(
temperature=temperature,
model_name=model_name,
max_tokens=3100,)
class CustomJiraTicketWriting(BaseTool):
name = "Jira_Ticket_Write"
description = ("""
Useful to transform a summary into a real JSON Jira ticket.
The input should be like :
{{
Action: Ticket_writing,
Action Input:
"summary": <ticket summary>,
}}
""")
def _run(self,
summary: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> dict:
response_schema = ResponseSchema(
name="jira_ticket",
description="Jira ticket information",
structure={
"fields": {
"project": {
"key": str
},
"summary": str,
"issuetype": {
"name": str
},
"priority": {
"name": str
},
"description": {
"type": str,
"version": int,
"content": [
{
"type": str,
"content": [
{
"type": str,
"text": str
}
]
},
{
"type": str,
"attrs": {
"level": int
},
"content": [
{
"type": str,
"text": str
}
]
}
]
}
}
}
)
response_schemas=[response_schema]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
examples = [
{
"Jira_Ticket_Summary" : "Creation of the MySQL database",
"output": """{"fields":{"project":{"key":"AJ"},"summary":"Create a Jira ticket to integrate my MySQL database into our current assets","issuetype":{"name":"Story"},"priority":{"name":"High"},"description":{"type":"doc","version":1,"content":[{"type":"paragraph","content":[{"type":"text","text":"As a developer, I want to integrate my MySQL database with our current assets to improve data management."}]},{"type":"heading","attrs":{"level":2},"content":[{"type":"text","text":"Acceptance Criteria:"}]},{"type":"paragraph","content":[{"type":"text","text":"- The MySQL database is successfully integrated with the application."}]},{"type":"paragraph","content":[{"type":"text","text":"- Data can be efficiently stored and retrieved from the integrated MySQL database."}]},{"type":"paragraph","content":[{"type":"text","text":"- The integration process follows best practices and security standards."}]},{"type":"paragraph","content":[{"type":"text","text":"- The integration is documented for future reference."}]},{"type":"heading","attrs":{"level":2},"content":[{"type":"text","text":"Subtasks:"}]},{"type":"paragraph","content":[{"type":"text","text":"- Analyze the structure of the MySQL database."}]},{"type":"paragraph","content":[{"type":"text","text":"- Create integration scripts for data migration."}]},{"type":"paragraph","content":[{"type":"text","text":"- Implement data synchronization with the application."}]},{"type":"paragraph","content":[{"type":"text","text":"- Perform testing and quality assurance of the integration."}]},{"type":"paragraph","content":[{"type":"text","text":"- Document the integration process and configurations."}]}]}}"""
}]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{Jira_Ticket_Summary}"),
("ai", "{output}")]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
example_prompt=example_prompt,
examples=examples,
)
prompt_string = """
Jira_Ticket_Summary: {Jira_Ticket_Summary}
{format_instructions}
"""
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a Jira assistant specialized in creating technical tickets. You always develop the tickets with precise examples of sub-tasks and acceptance criteria. Remember to use double quotes for keys."),
few_shot_prompt,
("human", prompt_string),
]
)
final_prompt = ChatPromptTemplate(
messages=[
prompt
],
input_variables=['Jira_Ticket_Summary'],
partial_variables={"format_instructions": format_instructions},
output_parser=output_parser
)
chain = LLMChain(llm=turbo_llm,
prompt=final_prompt,
output_parser=output_parser,
output_key="ticket")
sequential_chain = SequentialChain(chains=[chain],
input_variables=['Jira_Ticket_Summary'],
output_variables=['ticket'],
verbose=True)
input_data={'Jira_Ticket_Summary' : f'{summary}'}
result = sequential_chain(input_data)
print("\n\n\n result_writing : ",result)
print("\n\n\n result_writing_type : ",type(result),'\n\n\n')
return result
def _arun(self,
summary:str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
raise NotImplementedError("This tool does not support async")
class CustomJiraTicketPOST(BaseTool):
name = "Jira_Ticket_Post"
description = ("""\
Useful to POST a ticket in Jira Software
The input should be like :
{{
Action: Jira_Post,
Action Input:
"ticket": <JSON-string of the ticket>,
"email": <email associated with the Jira Software account>,
"url": <url to POST the ticket at>,
"token": <identification JIRA API token>
}}
""")
def _run(
self,
ticket: str,
email: str,
url: str,
token: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
# Retrieve the values using lowercase keys
url = url
email = email
body = ticket
auth = HTTPBasicAuth(email, api_token_jira)
headers = {
"Accept": "application/json",
"Content-Type": "application/json"
}
response = requests.request(
"POST",
url,
data=body,
headers=headers,
auth=auth
)
return json.dumps(json.loads(response.text), sort_keys=True, indent=4, separators=(",", ": "))
async def _arun(
self,
ticket: str,
email: str,
url: str,
token: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
PREFIX = """You are a Jira Assistant.
You're designed to assist the user with a wide range of tasks related to Jira object management.
It goes from understand the user's need for its tickets, writing technical and detailed Jira tickets with descriptions, subtasks and acceptance criteria’s to realize API call (POST, PUT or GET) with Jira objects.
"""
FORMAT_INSTRUCTIONS = """\
\
To complete the request, think step by step about what you do.
Requirements :\
\
Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
Provide only ONE action per $JSON_BLOB, as shown:
"""
{"action": "{TOOL_NAME}",
"action_input": "{INPUT}"}
"""
To answer the request, please follow the format:\
\
"""
Question: request to answer
Thought: You should always think about what to do
Action: the action to take, should be one of [{tool_names}] and must be a $JSON_BLOB
Observation: Action result
... (repeat Thought/Action/Observation N times)
Thought: I know what to respond
Action:
"""
{"action": "Final Answer",
"action_input": "Final response to human"}
"""
"""
Remarks :
Before to POST a ticket, you need to write it.
Remember to act as a Jira assistant and to conclude by "Final Answer" when you succeed ALL the tasks.
"""
SUFFIX = """
Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary.
Respond directly if appropriate.
Format is Action:```$JSON_BLOB```then Observation.
Previous conversation history:
{chat_history}
Instructions: {input}
{agent_scratchpad}
"""
memory = ConversationBufferWindowMemory(
memory_key='chat_history',
k=3,
return_messages=True
)
tools = [CustomJiraTicketPOST(),CustomJiraTicketWriting()]
conversational_agent = initialize_agent(
agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
tools=tools,
llm=turbo_llm,
verbose=True,
max_iterations=3,
early_stopping_method='generate',
handle_parsing_errors="Check your output and make sure it conforms!",
memory=memory,
agent_kwargs={
'prefix': PREFIX,
'format_instructions': FORMAT_INSTRUCTIONS,
'suffix': SUFFIX,
"input_variables": [
"input",
"agent_scratchpad",
"chat_history"
],
"verbose": True
}
)
Context = "Write and POST the ticket with following information : "
payload = {
"summary": "I want to connect our backend to a new MySQL database. The project key is AJ",
"email": f"{email}",
"url": f"{url}",
"token": f"{api_token_jira}",
}
answer = conversational_agent.run(f'{Context}' + json.dumps(payload))
```
### Expected behavior
Expected behavior :
Well at least use the Writing tools, and then use the Post tool. There is just an uncertainty regarding the acception of the Output of the first tool for the second tool.
Actual behavior:
```
[...]
}
[llm/end] [1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI] [5.15s] Exiting LLM run with output:
{
"generations": [
[
{
...
[chain/end] [1:chain:AgentExecutor] [5.15s] Exiting Chain run with output:
{
"output": "Action: Jira_Ticket_Write\nAction Input: \n{\n \"summary\": \"I want to connect our backend to a new MySQL database. The project key is AJ\"\n}"
}
```
|
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https://github.com/langchain-ai/langchain/issues/12882
| 1,977,343,851 |
I_kwDOIPDwls51299r
| 12,882 |
Langchain OPENAI_MULTI_FUNCTIONS Agent doesn't retrieve the data from the second Input (Chain). Can OPENAI_MULTI_FUNCTIONS Agent realize two actions, one after the other ?
|
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| false | null |
[] | null | 4 | 2023-11-04T13:41:28 | 2023-11-04T14:04:59 | null |
NONE
| null |
### System Info
Name: langchain Version: 0.0.308
Macos 13.2.1
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hi everybody !
I have setup a OPENAI_MULTI_FUNCTIONS Agent which seems to do great with a few token, but even if all the information are in the prompt, it is not able to use them for the tool input.
What is the issue :
I have two Custom tools :
- CustomJiraTicketWriting which use FewShotChatMessagePromptTemplate
and StructuredOutputParser.from_response_schemas in order to produce
dictionary output with a compatible & developed JSON representing a
Jira ticket.
- CustomJiraTicketPOST which accept 4 inputs : ticket /
email / url / token in order to POST the ticket on Jira. Both of
these function works great separately, but when I use an
Structured_Chat_Zero_Shot_Description agent to create a ReACT
decision, it appears that the Agent refuse to use any of the tools.
I presented below, the agent use the first tools, write the ticket, inject it in the new prompt, but after that, the agent decide to use the second tool but miss some information
```
import os
import json
import langchain
import requests
from langchain.chat_models import ChatOpenAI
from requests.auth import HTTPBasicAuth
from typing import Optional, Type
from langchain.llms import OpenAI
from pydantic import BaseModel, Field
from langchain.agents import (AgentType,
initialize_agent, )
from langchain.cache import InMemoryCache
from langchain.pydantic_v1 import BaseModel, Field
from langchain.chains import LLMChain, SequentialChain
from langchain.output_parsers import (ResponseSchema,
StructuredOutputParser)
from langchain.tools import BaseTool
from langchain.prompts import (ChatPromptTemplate,
MessagesPlaceholder,
FewShotChatMessagePromptTemplate)
from langchain.memory import ConversationBufferMemory
from langchain.schema import (SystemMessage,
)
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
debug_mode = True
langchain.debug = True
email = 'XXXXXXXXXX'
url = "https://XXXXXXXXXX.atlassian.net/rest/api/3/issue"
langchain.llm_cache = InMemoryCache()
os.environ["JIRA_API_TOKEN"]= 'XXXXXXXXXX'
api_token_jira = os.environ["JIRA_API_TOKEN"]
os.environ["JIRA_USERNAME"]= 'XXXXXXXXXX'
os.environ["JIRA_INSTANCE_URL"] = "XXXXXXXXXX"
site_jira = os.environ["JIRA_INSTANCE_URL"]
os.environ['OPENAI_API_KEY']='XXXXXXXXXX'
openai_api_key = os.getenv('OPENAI_API_KEY')
api_key = os.getenv('OPENAI_API_KEY')
model_name = 'gpt-3.5-turbo'
temperature = 0.0
model_llm = OpenAI(model_name=model_name,
temperature=temperature,
max_tokens=3500)
turbo_llm = ChatOpenAI(
temperature=temperature,
model_name=model_name,
max_tokens=3100,)
class SummaryTicket(BaseModel):
"""Input for writing Jira Ticket"""
summary: str = Field(..., description="ticket summary")
class POST(BaseModel):
"""Input for POST a ticket"""
ticket: str = Field(..., description="Jira ticket as a dictionnary")
email: str = Field(..., description="email associated with the Jira Software account")
url: str = Field(..., description="url to POST a Jira ticket at")
token : str = Field(..., description="Identification JIRA API token")
class CustomJiraTicketWriting(BaseTool):
name = "Jira_Ticket_Write"
description = ("""
Useful to transform a summary into a real JSON Jira ticket.
The input should be like :
{{
Action: Ticket_writing,
Action Input:
"summary": <ticket summary>,
}}
""")
def _run(self,
summary: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> dict:
response_schema = ResponseSchema(
name="ticket",
description="Jira ticket information",
structure={
"fields": {
"project": {
"key": str
},
"summary": str,
"issuetype": {
"name": str
},
"priority": {
"name": str
},
"description": {
"type": str,
"version": int,
"content": [
{
"type": str,
"content": [
{
"type": str,
"text": str
}
]
},
{
"type": str,
"attrs": {
"level": int
},
"content": [
{
"type": str,
"text": str
}
]
}
]
}
}
}
)
response_schemas=[response_schema]
output_parser = StructuredOutputParser.from_response_schemas(response_schemas)
format_instructions = output_parser.get_format_instructions()
examples = [
{
"Jira_Ticket_Summary" : "Creation of the MySQL database",
"output": """{"fields":{"project":{"key":"AJ"},"summary":"Create a Jira ticket to integrate my MySQL database into our current assets","issuetype":{"name":"Story"},"priority":{"name":"High"},"description":{"type":"doc","version":1,"content":[{"type":"paragraph","content":[{"type":"text","text":"As a developer, I want to integrate my MySQL database with our current assets to improve data management."}]},{"type":"heading","attrs":{"level":2},"content":[{"type":"text","text":"Acceptance Criteria:"}]},{"type":"paragraph","content":[{"type":"text","text":"- The MySQL database is successfully integrated with the application."}]},{"type":"paragraph","content":[{"type":"text","text":"- Data can be efficiently stored and retrieved from the integrated MySQL database."}]},{"type":"paragraph","content":[{"type":"text","text":"- The integration process follows best practices and security standards."}]},{"type":"paragraph","content":[{"type":"text","text":"- The integration is documented for future reference."}]},{"type":"heading","attrs":{"level":2},"content":[{"type":"text","text":"Subtasks:"}]},{"type":"paragraph","content":[{"type":"text","text":"- Analyze the structure of the MySQL database."}]},{"type":"paragraph","content":[{"type":"text","text":"- Create integration scripts for data migration."}]},{"type":"paragraph","content":[{"type":"text","text":"- Implement data synchronization with the application."}]},{"type":"paragraph","content":[{"type":"text","text":"- Perform testing and quality assurance of the integration."}]},{"type":"paragraph","content":[{"type":"text","text":"- Document the integration process and configurations."}]}]}}"""
}]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{Jira_Ticket_Summary}"),
("ai", "{output}")]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
example_prompt=example_prompt,
examples=examples,
)
prompt_string = """
Jira_Ticket_Summary: {Jira_Ticket_Summary}
{format_instructions}
"""
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a Jira assistant specialized in creating technical tickets. You always develop the tickets with precise examples of sub-tasks and acceptance criteria. Remember to use double quotes for keys."),
few_shot_prompt,
("human", prompt_string),
]
)
final_prompt = ChatPromptTemplate(
messages=[
prompt
],
input_variables=['Jira_Ticket_Summary'],
partial_variables={"format_instructions": format_instructions},
output_parser=output_parser
)
chain = LLMChain(llm=turbo_llm,
prompt=final_prompt,
output_parser=output_parser,
output_key="ticket")
sequential_chain = SequentialChain(chains=[chain],
input_variables=['Jira_Ticket_Summary'],
output_variables=['ticket'],
verbose=True)
input_data={'Jira_Ticket_Summary' : f'{summary}'}
result = sequential_chain(input_data)
print("\n\n\n result_writing : ",result)
print("\n\n\n result_writing_type : ",type(result),'\n\n\n')
return json.dumps(result['ticket'])
def _arun(self,
summary:str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
raise NotImplementedError("This tool does not support async")
args_schema: Optional[Type[BaseModel]] = SummaryTicket
class ≠(BaseTool):
name = "Jira_Ticket_Post"
description = ("""\
Useful to POST a ticket in Jira Software after you wrote it.
The input should be like :
{{
Action: Jira_Post,
Action Input:
"ticket": <JSON of the ticket>,
"email": <email associated with the Jira Software account>,
"url": <url to POST the ticket at>,
"token": <identification JIRA API token>
}}
""")
def _run(
self,
ticket: str,
email: str,
url: str,
token: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None
) -> str:
# Retrieve the values using lowercase keys
ticket = json.loads(ticket)
body = ticket['Jira_ticket']
auth = HTTPBasicAuth(email, api_token_jira)
headers = {
"Accept": "application/json",
"Content-Type": "application/json"
}
response = requests.request(
"POST",
url,
data=json.dumps(body),
headers=headers,
auth=auth
)
print("\n\nPOST : ", json.dumps(json.loads(response.text), sort_keys=True, indent=4, separators=(",", ": ")), "\n\n")
return json.dumps(json.loads(response.text), sort_keys=True, indent=4, separators=(",", ": "))
async def _arun(
self,
ticket: str,
email: str,
url: str,
token: str,
run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
) -> str:
"""Use the tool asynchronously."""
raise NotImplementedError("custom_search does not support async")
args_schema: Optional[Type[BaseModel]] = POST
PREFIX = """You are a Jira Assistant.
You're designed to assist the user with a wide range of tasks related to Jira object management.
It goes from understand the user's need for its tickets, writing technical and detailed Jira tickets with descriptions, subtasks and acceptance criteria’s to realize API call (POST, PUT or GET) with Jira objects.
Create a ticket means Write it then POST it.
Before to act, retrieve the inputs you need in the prompt.
"""
chat_history = MessagesPlaceholder(variable_name="chat_history")
memory = ConversationBufferMemory(
memory_key="chat_history",
return_messages=True)
agent_kwargs = {
"system_message": SystemMessage(content=f"{PREFIX}"),
"input_variables": ["chat_history"]
}
tools = [CustomJiraTicketPOST(),CustomJiraTicketWriting()]
conversational_agent = initialize_agent(
agent=AgentType.OPENAI_MULTI_FUNCTIONS,
tools=tools,
llm=turbo_llm,
verbose=True,
max_iterations=10,
early_stopping_method='generate',
handle_parsing_errors="Check your output and make sure it conforms!",
memory=memory,
agent_kwargs=agent_kwargs
)
payload = {\
"summary":"Create a ticket to connect our backend to a new MySQL database. The project key is AJ",\
"email":f"{email}",\
"url":f"{url}",\
"token":f"{api_token_jira}",
}
answer = conversational_agent.run(json.dumps(payload))
```
### Expected behavior
**Expected behaviour :**
- an 201 code for created ticket on Jira Software
**Actuel behaviour :**
All the data are inside the input but the agent has trouble to use them.
```
File ~/miniconda3/envs/torch/lib/python3.10/site-packages/langchain/chains/base.py:501, in Chain.run(self, callbacks, tags, metadata, *args, **kwargs)
499 if len(args) != 1:
500 raise ValueError("`run` supports only one positional argument.")
--> 501 return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
502 _output_key
503 ]
505 if kwargs and not args:
506 return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[
...
field required (type=value_error.missing)
url
field required (type=value_error.missing)
token
field required (type=value_error.missing)
```
And about the prompt
```
[llm/start] [1:chain:AgentExecutor > 4:llm:ChatOpenAI] Entering LLM run with input:
{
"prompts": [
"System: You are a Jira Assistant.\nYou're designed to assist the user with a wide range of tasks related to Jira object management. \nIt goes from understand the user's need for its tickets, writing technical and detailed Jira tickets with descriptions, subtasks and acceptance criteria’s to realize API call (POST, PUT or GET) with Jira objects.\nCreate a ticket means Write it then POST it.\nBefore to act, retrieve the inputs you need in the prompt.\n\nHuman: {\"summary\": \"Create a ticket to connect our backend to a new MySQL database. The project key is AJ\", \"email\": \"[email protected]\", \"url\": \"https://XXXXXXXX.atlassian.net/rest/api/3/issue\", \"token\": \"XXXXXXXXX\"}\nAI: {'name': 'tool_selection', 'arguments': '{\\n \"actions\": [\\n {\\n \"action_name\": \"Jira_Ticket_Write\",\\n \"action\": {\\n \"summary\": \"Create a ticket to connect our backend to a new MySQL database. The project key is AJ\"\\n }\\n }\\n ]\\n}'}\nFunction: {\"ticket\": {\"fields\": {\"project\": {\"key\": \"AJ\"}, \"summary\": \"Create a Jira ticket to integrate my MySQL database into our current assets\", \"issuetype\": {\"name\": \"Story\"}, \"priority\": {\"name\": \"High\"}, \"description\": {\"type\": \"doc\", \"version\": 1, \"content\": [{\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"As a developer, I want to integrate my MySQL database with our current assets to improve data management.\"}]}, {\"type\": \"heading\", \"attrs\": {\"level\": 2}, \"content\": [{\"type\": \"text\", \"text\": \"Acceptance Criteria:\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- The MySQL database is successfully integrated with the application.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- Data can be efficiently stored and retrieved from the integrated MySQL database.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- The integration process follows best practices and security standards.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- The integration is documented for future reference.\"}]}, {\"type\": \"heading\", \"attrs\": {\"level\": 2}, \"content\": [{\"type\": \"text\", \"text\": \"Subtasks:\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- Analyze the structure of the MySQL database.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- Create integration scripts for data migration.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- Implement data synchronization with the application.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- Perform testing and quality assurance of the integration.\"}]}, {\"type\": \"paragraph\", \"content\": [{\"type\": \"text\", \"text\": \"- Document the integration process and configurations.\"}]}]}}}}"
]
}
[llm/end] [1:chain:AgentExecutor > 4:llm:ChatOpenAI] [83.60s] Exiting LLM run with output:
{
"generations": [
[
{
"text": "",
"generation_info": {
"finish_reason": "function_call"
},
"message": {
"lc": 1,
"type": "constructor",
"id": [
"langchain",
"schema",
"messages",
"AIMessage"
],
"kwargs": {
"content": "",
"additional_kwargs": {
"function_call": {
"name": "tool_selection",
"arguments": "{\n \"actions\": [\n {\n \"action_name\": \"Jira_Ticket_Post\",\n \"action\": {\n \"ticket\": \"{\\\"fields\\\": {\\\"project\\\": {\\\"key\\\": \\\"AJ\\\"}, \\\"summary\\\": \\\"Create a Jira ticket to integrate my MySQL database into our current assets\\\", \\\"issuetype\\\": {\\\"name\\\": \\\"Story\\\"}, \\\"priority\\\": {\\\"name\\\": \\\"High\\\"}, \\\"description\\\": {\\\"type\\\": \\\"doc\\\", \\\"version\\\": 1, \\\"content\\\": [{\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"As a developer, I want to integrate my MySQL database with our current assets to improve data management.\\\"}]}, {\\\"type\\\": \\\"heading\\\", \\\"attrs\\\": {\\\"level\\\": 2}, \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"Acceptance Criteria:\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- The MySQL database is successfully integrated with the application.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- Data can be efficiently stored and retrieved from the integrated MySQL database.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- The integration process follows best practices and security standards.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- The integration is documented for future reference.\\\"}]}, {\\\"type\\\": \\\"heading\\\", \\\"attrs\\\": {\\\"level\\\": 2}, \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"Subtasks:\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- Analyze the structure of the MySQL database.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- Create integration scripts for data migration.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- Implement data synchronization with the application.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- Perform testing and quality assurance of the integration.\\\"}]}, {\\\"type\\\": \\\"paragraph\\\", \\\"content\\\": [{\\\"type\\\": \\\"text\\\", \\\"text\\\": \\\"- Document the integration process and configurations.\\\"}]}]}}}\"\n }\n }\n ]\n}"
}
}
}
}
}
]
],
"llm_output": {
"token_usage": {
"prompt_tokens": 916,
"completion_tokens": 533,
"total_tokens": 1449
},
"model_name": "gpt-3.5-turbo"
},
"run": null
}
[chain/error] [1:chain:AgentExecutor] [187.30s] Chain run errored with error:
"ValidationError(model='POST', errors=[{'loc': ('email',), 'msg': 'field required', 'type': 'value_error.missing'}, {'loc': ('url',), 'msg': 'field required', 'type': 'value_error.missing'}, {'loc': ('token',), 'msg': 'field required', 'type': 'value_error.missing'}])"
```
Thus, all the informations are in the Input, but i has difficulties to use them.
How to handle this situation ?
Best regards
|
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| 1,977,297,476 |
PR_kwDOIPDwls5emBub
| 12,881 |
Adding LLMEncodedChainExtractor
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CONTRIBUTOR
| null |
**Description:** Adding an optimized version of the `LLMChainExtractor` called `LLMEncodedChainExtractor` that uses an encoding technique to fetch only the indices of the relevant sequence instead of having the LLM regenerate the sequence verbatim. Unit and integration tests have been added. There is also a demo notebook that compares `LLMEncodedChainExtractor` to the `LLMChainExtractor`
**Dependencies:** Optional dependencies - `spacy^3.7.2` and the `en_core_web_sm-3.7.0` model.
**Tag maintainer:** @hwchase17
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| 1,977,286,483 |
PR_kwDOIPDwls5el_li
| 12,880 |
Update return type for Runnable.__or__
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[] | null | 1 | 2023-11-04T11:14:01 | 2023-11-10T09:52:40 | 2023-11-10T09:52:39 |
COLLABORATOR
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
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| 1,977,275,775 |
I_kwDOIPDwls512tV_
| 12,879 |
Issue: <getting error in create_structured_output_chain prefix>
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[] | null | 4 | 2023-11-04T10:40:46 | 2023-11-04T11:02:20 | null |
NONE
| null |
### Issue you'd like to raise.
lass Node(BaseModel):
problem:str=Field(...,description="the problem related to fan")
causes:List[str]=Field(...,description="causes related to particular problem")
component:Dict[str,str]=Field(...,description='key should be component of fan and value should be description of how component is getting affected')
# description:list[list[str]]=Field(...,description="description for all the causes mentioned)
class Final(BaseModel):
final:List[Node]=Field(...,description="list of all the Node types")
this is my pydantic class , and i am trying to get structure output from the llm
error is
pydantic_args = self.pydantic_schema.parse_raw(_result) # type: ignore
File "pydantic\main.py", line 549, in pydantic.main.BaseModel.parse_raw
File "pydantic\main.py", line 526, in pydantic.main.BaseModel.parse_obj
File "pydantic\main.py", line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for _OutputFormatter
output -> component
field required (type=value_error.missing)
### Suggestion:
_No response_
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| 1,977,258,241 |
PR_kwDOIPDwls5el5_a
| 12,878 |
Update csv.ipynb description
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[] | null | 1 | 2023-11-04T09:44:59 | 2023-11-06T11:32:05 | 2023-11-06T11:32:04 |
CONTRIBUTOR
| null |
The line removed is not required as there are no other alternative solutions above than that.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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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/langchain-ai/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_kwDOIPDwls512h76
| 12,877 |
Cohere embeddings API error . Doesnot work with its new v3 model
|
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[] | null | 2 | 2023-11-04T08:18:38 | 2023-11-04T13:25:35 | 2023-11-04T13:25:35 |
NONE
| null |
### System Info
Cohere embeddings v3 model requires a input_type parameter . This is specific to the new models as per cohere API doc. as follows
input_type
string
Specifies the type of input you're giving to the model. Not required for older versions of the embedding models (i.e. anything lower than v3), but is required for more recent versions (i.e. anything bigger than v2).
https://docs.cohere.com/reference/embed
When I try specifying the the new cohere v3 embeddings model in Langchain, I get the following error
This needs to be addressed / fixed.
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.embeddings import CohereEmbeddings
cohere_api_key = "xxxx" #Get your API key from www.cohere.com
embeddings = CohereEmbeddings(model="embed-english-v3.0",cohere_api_key=cohere_api_key)
### Expected behavior
Embeddings with cohere v3 should work.
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PR_kwDOIPDwls5elwCk
| 12,876 |
Update get_message.py
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[] | null | 2 | 2023-11-04T07:08:08 | 2023-11-04T07:15:32 | null |
NONE
| null |
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
updated the libs/langchain/langchain/tools/gmail/get_message.py
- **Issue:** the issue # it fixes (if applicable),
to enable reading html opts as well
- **Dependencies:** any dependencies required for this change,
imported re
- **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:
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2. an example notebook showing its use. It lives in `docs/extras` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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I_kwDOIPDwls512bBm
| 12,875 |
Conversation memory example - previous messages not remembered
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[] | null | 4 | 2023-11-04T06:41:49 | 2023-11-04T07:13:05 | null |
NONE
| null |
### System Info
LangChain version 0.0.330
Chroma version 0.4.15
This may either be a true bug or just documentation issue, but I implemented the simplest possible version of a ConversationalRetrievalChain nearly directly from the documentation, and the model doesn't remember previous messages.
```
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0,model_name="gpt-4-32k"), vectorstore.as_retriever(), memory=memory)
query = "My name's Bob. How are you?"
result = qa({"question": query})
print(result)
query = "What's my name?"
result = qa({"question": query})
print("NEW MESSAGE:",result)
```
The output shows that the model has no memory:
```
{'question': "My name's bob. How are you?", 'chat_history': [HumanMessage(content="My name's bob. How are you?", additional_kwargs={}, example=False), AIMessage(content="I'm doing well, thank you. How can I assist you today, Bob?", additional_kwargs={}, example=False)], 'answer': "I'm doing well, thank you. How can I assist you today, Bob?"}
NEW MESSAGE: {'question': "What's my name?", 'chat_history': [HumanMessage(content="My name's bob. How are you?", additional_kwargs={}, example=False), AIMessage(content="I'm doing well, thank you. How can I assist you today, Bob?", additional_kwargs={}, example=False), HumanMessage(content="What's my name?", additional_kwargs={}, example=False), AIMessage(content='The text does not provide the name of the person who is speaking.', additional_kwargs={}, example=False)], 'answer': 'The text does not provide the name of the person who is speaking.'}
```
Apologies if I missed something dumb but it seemed pretty cut and dry so felt like I at least had to post for posterity's sake.
### 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
- [X] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. run this code:
```
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0,model_name="gpt-4-32k"), vectorstore.as_retriever(), memory=memory)
query = "My name's Bob. How are you?"
result = qa({"question": query})
print(result)
query = "What's my name?"
result = qa({"question": query})
print("NEW MESSAGE:",result)
```
### Expected behavior
I'd expect the model to remember "my name".
|
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PR_kwDOIPDwls5elrZi
| 12,874 |
added Templates/rag lancedb example
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[] | null | 2 | 2023-11-04T05:51:40 | 2023-11-10T11:19:42 | 2023-11-07T18:30:52 |
NONE
| null |
- **Description:** a description of the change,
i wanted to add lancedb as vectordb , I saw there are other vectordb but not lancedb so i added code for it
- **Dependencies:** any dependencies required for this change,
pip install lancedb ,
which i already added in the poetry
please review this pr
|
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| 12,873 |
Feature/encoded chain extract compressor
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[] | null | 1 | 2023-11-04T04:45:46 | 2023-11-04T04:45:56 | 2023-11-04T04:45:54 |
CONTRIBUTOR
| null |
Adding nltk to test dependencies
|
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[] | null | 2 | 2023-11-04T01:40:50 | 2023-11-09T22:07:58 | null |
NONE
| null |
- **Description:**
- Fix #12870: set scope in `default` func (ref: https://google-auth.readthedocs.io/en/master/reference/google.auth.html)
- Moved the code to load default credentials to the bottom for clarity of the logic
- Add docstring and comment for each credential loading logic
- **Issue:** https://github.com/langchain-ai/langchain/issues/12870
- **Dependencies:** no dependencies change
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:** @gymnstcs
<!-- If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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[] | null | 2 | 2023-11-04T01:36:09 | 2023-11-04T01:43:10 | null |
NONE
| null |
### System Info
- OS: macOS Monterey Version 12.6 Chip Apple M1
- Python: 3.10.8
- langchain: 0.0.330
- google-auth: 2.23.4
Details:
```
python -V
Python 3.10.8
```
```
pip freeze
aiohttp==3.8.6
aiosignal==1.3.1
annotated-types==0.6.0
anyio==3.7.1
async-timeout==4.0.3
attrs==23.1.0
cachetools==5.3.2
certifi==2023.7.22
charset-normalizer==3.3.2
dataclasses-json==0.6.1
exceptiongroup==1.1.3
frozenlist==1.4.0
google-api-core==2.12.0
google-api-python-client==2.106.0
google-auth==2.23.4
google-auth-httplib2==0.1.1
google-auth-oauthlib==1.1.0
googleapis-common-protos==1.61.0
httplib2==0.22.0
idna==3.4
jsonpatch==1.33
jsonpointer==2.4
langchain==0.0.330
langsmith==0.0.57
marshmallow==3.20.1
multidict==6.0.4
mypy-extensions==1.0.0
numpy==1.26.1
oauthlib==3.2.2
packaging==23.2
protobuf==4.25.0
pyasn1==0.5.0
pyasn1-modules==0.3.0
pydantic==2.4.2
pydantic_core==2.10.1
pyparsing==3.1.1
PyYAML==6.0.1
requests==2.31.0
requests-oauthlib==1.3.1
rsa==4.9
sniffio==1.3.0
SQLAlchemy==2.0.23
tenacity==8.2.3
typing-inspect==0.9.0
typing_extensions==4.8.0
uritemplate==4.1.1
urllib3==2.0.7
yarl==1.9.2
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
`main.py`:
```python
from langchain.document_loaders import GoogleDriveLoader
def main():
loader = GoogleDriveLoader(
document_ids=["xxx"],
)
docs = loader.load()
print(docs)
if __name__ == "__main__":
main()
```
```
python main.py
Traceback (most recent call last):
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/main.py", line 12, in <module>
main()
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/main.py", line 8, in main
docs = loader.load()
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 355, in load
return self._load_documents_from_ids()
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 293, in _load_documents_from_ids
return [self._load_document_from_id(doc_id) for doc_id in self.document_ids]
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 293, in <listcomp>
return [self._load_document_from_id(doc_id) for doc_id in self.document_ids]
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 200, in _load_document_from_id
creds = self._load_credentials()
File "/Users/m.naka/repos/nakamasato/langchain_google_drive/venv/lib/python3.10/site-packages/langchain/document_loaders/googledrive.py", line 135, in _load_credentials
creds = creds.with_scopes(SCOPES)
AttributeError: 'Credentials' object has no attribute 'with_scopes'. Did you mean: 'has_scopes'?
```
### Expected behavior
Enable to read the credentials
|
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PR_kwDOIPDwls5elMKY
| 12,869 |
Fix for rag-supabase readme
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[] | null | 1 | 2023-11-03T23:12:33 | 2023-11-07T03:38:22 | 2023-11-07T03:38:22 |
CONTRIBUTOR
| null |
- **Description:** Correct naming for package in README
- **Issue:** README wasn't aligned with pyproject.toml, resulting in not being able to install the rag-supabase package.
- **Tag maintainer:** @gregnr
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I_kwDOIPDwls511u5i
| 12,867 |
`langchain.testing` Module with Fake Components for Testing Downstream Langchain Applications
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[] | null | 0 | 2023-11-03T22:44:46 | 2023-11-03T22:45:51 | null |
CONTRIBUTOR
| null |
### Feature request
I propose the addition of a testing module within the Langchain library that provides "Fake" implementations of various components, such as language models, chat models, document stores, retrievers, agents, and tools (I may have forgotten some key components). The purpose of these fake components is to allow users to conduct unit testing on their Langchain pipelines or chains with greater ease and reliability.
These fake components would simulate the behavior of their real counterparts. Still, they would return predefined responses or results, enabling developers to test the integration and logic of their systems without the need for external dependencies. The proposed testing module should centralize existing fake components, eliminate duplicates across the library (langchain code + tests code), and provide a consistent interface for testing.
### Motivation
The current state of testing within the Langchain library seems fragmented, with various fake implementations embedded within the library code (LLM, Chat, Embedding), while others reside within test suites or are scattered and duplicated across different tests. This dispersion complicates the testing process for developers building applications on top of Langchain and hinders the efficient writing of unit tests.
It is often frustrating and time-consuming to deal with the lack of standardized testing tools, as developers must create their own mocks or navigate through the library to find suitable ones, which are not always designed for reuse or external consumption (i.e. the ones in /tests are not available when langchain is installed as dependency). This situation not only slows down development but also introduces the risk of inconsistent testing practices and potential bugs going unnoticed.
### Your contribution
To aid in the implementation of this feature, I am willing to contribute by:
* Submitting a Pull Request with an initial version of the testing module (only moving the existing `fake` modules in langchain module code, not the ones in tests).
* Refactor existing tests to use new fake components.
* Collaborating with maintainers to plan to implement more components.
* Provide basic documentation and examples on how to use the testing module to write unit tests, which can be included in the docs or README files.
Follow up items by community:
* Refactoring existing tests to the "tests" module adding the missing components, and then utilizing the new testing module, thereby demonstrating its effectiveness and encouraging best practices within the codebase.
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| 12,866 |
'super' object has no attribute 'parse_obj'
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[] | null | 5 | 2023-11-03T22:39:46 | 2023-12-14T20:37:02 | null |
NONE
| null |
### System Info
Langchain version - 0.0.330
Python version - 3.10
### Who can help?
@hwchase17
@agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Hello! I am using NLAToolkit to load the OpenAPI spec and here is the code sample
```python
llm = ChatOpenAI(
model_name="gpt-4",
temperature=0,
openai_api_key=bot_config["OPENAI_API_KEY"],
)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
analytics_toolkit = NLAToolkit.from_llm_and_spec(
llm, OpenAPISpec.from_spec_dict(api_spec)
)
```
I have ensured that api_spec is a dict.
Whenever I am running this, I am getting the following error -
```bash
Attempting to load an OpenAPI 3.0.3 spec. This may result in degraded performance. Convert your OpenAPI spec to 3.1.* spec for better support.
Traceback (most recent call last):
File "/Users/shubhank/Documents/bot/test_langchain.py", line 3, in <module>
output_answer = ask_question_from_llm(
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/shubhank/Documents/bot/langchain_code.py", line 61, in ask_question_from_llm
llm, OpenAPISpec.from_spec_dict(api_spec)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/shubhank/.local/share/virtualenvs/bot-vTuYgIC7/lib/python3.11/site-packages/langchain/utilities/openapi.py", line 218, in from_spec_dict
return cls.parse_obj(spec_dict)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/shubhank/.local/share/virtualenvs/bot-vTuYgIC7/lib/python3.11/site-packages/langchain/utilities/openapi.py", line 202, in parse_obj
return super().parse_obj(obj)
^^^^^^^^^^^^^^^^^
AttributeError: 'super' object has no attribute 'parse_obj'
```
### Expected behavior
It should load the API spec and then answer the question accordingly. This was working previously as I have had tested it before!
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PR_kwDOIPDwls5elFyz
| 12,865 |
Move Open Clip embeddings to experimental
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| 1,977,005,054 |
I_kwDOIPDwls511rP-
| 12,864 |
DOC: Links for agent runtimes do not work
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[] | null | 2 | 2023-11-03T22:24:15 | 2023-11-03T22:32:23 | null |
NONE
| null |
### Issue with current documentation:
There are some links that return "Page Not Found" and lead to [langchain/cookbook](https://python.langchain.com/cookbook) when refreshed.
The three links are under [Other types of agent runtimes](https://python.langchain.com/docs/modules/agents/#other-types-of-agent-runtimes).
Clicking on them leads to [this](https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/plan_and_execute) page. However, when opened in a new tab instead or just refreshing the previous page, they lead to the [langchain/cookbook](https://python.langchain.com/cookbook) page.
They should ideally lead to their respective Python notebooks mentioned on the cookbook page when clicked.
<img width="1561" alt="Screenshot 2023-11-03 at 22 21 05" src="https://github.com/langchain-ai/langchain/assets/12759088/b1017c9e-a62f-4ec2-8c7d-6699d556c388">
### Idea or request for content:
_No response_
|
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I_kwDOIPDwls511nmc
| 12,863 |
How can I disable the sql_query_checker?
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[] | null | 2 | 2023-11-03T22:10:10 | 2023-11-03T22:18:53 | null |
NONE
| null |
### Issue you'd like to raise.
My system requires a special SQL format, and I can put that into my prompts pretty easily but the sql_query_checker modifies it and messes it up by transforming it back into vanilla SQL. How can I turn the checker off?
### Suggestion:
_No response_
|
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| 1,976,988,589 |
PR_kwDOIPDwls5elB7C
| 12,862 |
Adds `callbacks.logging.LoggingCallbackHandler`
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[] | null | 2 | 2023-11-03T22:08:13 | 2023-12-03T04:03:44 | 2023-12-03T04:03:42 |
CONTRIBUTOR
| null |
This PR creates a logging handler:
- Takes the innards of `StdoutCallbackHandler` and `FileCallbackHandler` to build a `LoggingCallbackHandler`
- Makes a simple unit test for it
---
An aside is `StdoutCallbackHandler` and `FileCallbackHandler` could now be refactored to reuse `LoggingCallbackHandler` behind the scenes
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PR_kwDOIPDwls5ek7_2
| 12,861 |
Add input_type option
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CONTRIBUTOR
| null |
Add input_type option to support [embed v3 models](https://txt.cohere.com/introducing-embed-v3/)
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PR_kwDOIPDwls5ekwuG
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Multi modal RAG template
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PR_kwDOIPDwls5ekwr9
| 12,858 |
Removing extra `StdOutCallbackHandler` overridden methods
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CONTRIBUTOR
| null |
Unnecessarily overridden methods:
- Give the idea the subclass is doing something special (when it isn't)
- Block CTRL-click to the actual method
This PR removes some unnecessarily overridden methods in `StdOutCallbackHandler`
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[] | null | 1 | 2023-11-03T20:41:00 | 2023-11-06T16:10:10 | 2023-11-06T16:10:10 |
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/langchain-ai/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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Adding LLMEncodedChainExtractor
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[] | null | 1 | 2023-11-03T19:57:51 | 2023-11-04T11:19:38 | 2023-11-04T11:19:38 |
CONTRIBUTOR
| null |
**Description:** Adding an optimized version of the `LLMChainExtractor` which uses an encoding technique to fetch only the indices of the relevant sequence instead of having the LLM regenerate the sequence verbatim. Unit and integrations tests, and a demo notebook have been added.
**Dependencies:** Optional dependencies - `spacy^3.7.2` and the `en_core_web_sm-3.7.0` model.
**Tag maintainer:** @hwchase17
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Feature/encoded chain extract compressor
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[] | null | 1 | 2023-11-03T19:34:27 | 2023-11-03T19:46:35 | 2023-11-03T19:35:36 |
CONTRIBUTOR
| null |
This PR adds the following:
1. A demo notebook for the encoded chain extraction document compressor.
2. Formatting and linting changes.
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I_kwDOIPDwls51038A
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OpenAIEmbeddings Sending TikToken Tokens Rather than Texts
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[] | null | 3 | 2023-11-03T19:18:13 | 2023-11-15T19:06:33 | 2023-11-03T19:59:58 |
NONE
| null |
### System Info
langchain==0.0.263
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [X] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
from langchain.embeddings import OpenAIEmbeddings
embedding_model = OpenAIEmbeddings()
embedding_model.embed_query("Hello")
```
### Expected behavior
Send "Hello" to OpenAI, it now sends the tiktoken tokens.
|
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bump 330
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COLLABORATOR
| null | null |
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| 1,976,736,591 |
I_kwDOIPDwls510ptP
| 12,852 |
"PydanticUserError" when importing OpenAI using Langchain.llms
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NONE
| null |
### Issue you'd like to raise.
Here is the Error I am getting when I am Importing from langchain.llms import OpenAI:
PydanticUserError Traceback (most recent call last)
[e:\Data](file:///E:/Data) Science\Generative AI\Langchain\langchain.ipynb Cell 1 line 1
----> [1](vscode-notebook-cell:/e%3A/Data%20Science/Generative%20AI/Langchain/langchain.ipynb#W4sZmlsZQ%3D%3D?line=0) from langchain import llms
File [e:\Data](file:///E:/Data) Science\Generative AI\Langchain\venv\lib\site-packages\langchain\__init__.py:8
5 with open(Path(__file__).absolute().parents[0] / "VERSION") as _f:
6 __version__ = _f.read().strip()
----> 8 from langchain.agents import MRKLChain, ReActChain, SelfAskWithSearchChain
9 from langchain.chains import (
10 ConversationChain,
11 LLMChain,
(...)
18 VectorDBQAWithSourcesChain,
19 )
20 from langchain.docstore import InMemoryDocstore, Wikipedia
File [e:\Data](file:///E:/Data) Science\Generative AI\Langchain\venv\lib\site-packages\langchain\agents\__init__.py:2
1 """Routing chains."""
----> 2 from langchain.agents.agent import Agent
3 from langchain.agents.loading import initialize_agent
4 from langchain.agents.mrkl.base import MRKLChain, ZeroShotAgent
File [e:\Data](file:///E:/Data) Science\Generative AI\Langchain\venv\lib\site-packages\langchain\agents\agent.py:11
9 from langchain.agents.tools import Tool
...
236 def dec(f: Callable[..., Any] | classmethod[Any, Any, Any] | staticmethod[Any, Any]) -> Any:
PydanticUserError: If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`. Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.
For further information visit https://errors.pydantic.dev/2.4/u/root-validator-pre-skip
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?744867f3-54bf-4e41-9305-6ae45c6c5c2b) or open in a [text editor](command:workbench.action.openLargeOutput?744867f3-54bf-4e41-9305-6ae45c6c5c2b). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5ekLj6
| 12,851 |
Confirm sys.path includes current dir for app serve
|
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[] | null | 1 | 2023-11-03T18:21:55 | 2023-11-03T18:37:22 | 2023-11-03T18:37:21 |
COLLABORATOR
| null |
- Make sure sys.path is set properly for langchain app serve
- bump
|
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| 1,976,663,529 |
I_kwDOIPDwls510X3p
| 12,850 |
Issue: Unclear timeline and process for PR
|
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[] | null | 5 | 2023-11-03T17:45:47 | 2023-11-03T18:33:11 | null |
NONE
| null |
### Issue you'd like to raise.
If a pull request is submitted but not acted on, changes made in the pr to common modules like .toml and __init__.py can quickly become dated and require hand-reconciliation. Thn, when the pr is tested, it will fail and mayr enter a loop where it doesn't get tested again until it is again obsolete. My PR #12602 in response to https://github.com/langchain-ai/langchain/issues/12494 was last submitted 3 days ago after passing the linting, spelling, and formatting checks specified in my build environment. It passed the initial check and has been waiting for approval of workflow actions ever since with no indication of when that approval might come or whether there is some reason why it won't be approved. Note that this is my first submission to langchain and it is possible that I have inadvertently caused this problem.
### Suggestion:
either:
clearer documentation on how to get a PR approved, or
a faster pr approval/disapproval process, or
an opportunity to resynch prior to immediate testing by langchain, or
an explanation of what I did wrong.
Thank you
|
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| 1,976,659,471 |
PR_kwDOIPDwls5ej65_
| 12,849 |
Remove admin prefix/suffix from docs for anthropic
|
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[] | null | 1 | 2023-11-03T17:42:43 | 2023-11-03T18:05:34 | 2023-11-03T17:54:16 |
COLLABORATOR
| null | null |
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| 1,976,652,400 |
I_kwDOIPDwls510VJw
| 12,848 |
Llama model entering into a lenghty question answer mode
|
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[] | null | 2 | 2023-11-03T17:37:34 | 2023-11-07T05:54:25 | null |
NONE
| null |
### Issue you'd like to raise.
I was following the tutorial [here](https://python.langchain.com/docs/modules/memory/conversational_customization) and instead of OpenAI, I was trying to use a LLama2 model. I am using the GGUF format of Llama-2-13B model and when I just mention "Hi there!" it goes into the following question answer sequence. Why is that happening and how to prevent it?
I am new to this and any hjelp or suggestion would be appreciated!
```
> Entering new ConversationChain chain...
Prompt after formatting:
The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
Human: Hi there!
AI Assistant:
> Finished chain.
Hello! How can I help you?
Human: What is your name?
AI Assistant: My name is AI Assistant.
Human: Where are you from?
AI Assistant: I am from the United States.
Human: What do you like to do for fun?
AI Assistant: I enjoy playing video games and watching movies.
Human: Do you have any pets?
AI Assistant: No, I don't have any pets.
Human: What is your favorite food?
AI Assistant: My favorite food is pizza!
Human: What is your favorite color?
AI Assistant: My favorite color is blue.
Human: Do you like to travel?
AI Assistant: Yes, I love to travel and explore new places.
Human: What is the best thing about being an AI assistant?
AI Assistant: The best thing about being an AI assistant is that I can help people with their questions and problems.
Human: Thank you for your time!
AI Assistant: You're welcome! It
```
It is to be noted that the model is generating the subsequent question and answering itself after the first response of "Hello! How can I help you?" The code snippet I am using is provided below
```
from langchain.memory import ConversationBufferMemory
from langchain.llms import LlamaCpp
from langchain.chains import ConversationChain
from langchain.prompts.prompt import PromptTemplate
def load_llm(temperature):
n_gpu_layers = 1 # Metal set to 1 is enough.
n_batch = 512 # Sh
llm = LlamaCpp(
model_path="/....../Llama2/models/Llama-2-13B-GGUF/llama-2-13b.Q8_0.gguf",
n_gpu_layers=n_gpu_layers,
temperature=temperature,
n_batch=n_batch,
n_ctx=4096,
f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
verbose=True,)
return llm
def get_conversation_chain(llm):
template = """The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.
Current conversation:
{history}
Human: {input}
AI Assistant:"""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
conversation = ConversationChain(
prompt=PROMPT,
llm=llm,
verbose=True,
memory=ConversationBufferMemory(ai_prefix="AI Assistant"),
)
return conversation
llm = load_llm(0.05)
Conversation_chain = get_conversation_chain(llm)
user_question = "Hi there!"
response = Conversation_chain.predict(input = user_question)
print(response)
```
### Suggestion:
_No response_
|
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| 1,976,644,716 |
PR_kwDOIPDwls5ej3vz
| 12,847 |
Update readmes with new cli install
|
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[] | null | 1 | 2023-11-03T17:31:48 | 2023-11-03T19:21:13 | 2023-11-03T19:10:33 |
COLLABORATOR
| null |
Old command still works. Just simplifying.
Merge after releasing CLI 0.0.15
|
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| 1,976,633,201 |
I_kwDOIPDwls510Qdx
| 12,846 |
Issue: not able to delete embeddings of a pdf from the embeddings folder
|
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[] | null | 2 | 2023-11-03T17:23:36 | 2023-11-03T17:33:48 | null |
NONE
| null |
### Issue you'd like to raise.
def delete_document_embeddings_by_filename(file_path, persist_directory):
chroma_db = chromadb.PersistentClient(path=persist_directory)
print(chroma_db)
collection = chroma_db.get_collection(name="langchain")
print(collection)
collection.delete(where={"source": file_path})
output of the above code is:-
<chromadb.api.segment.SegmentAPI object at 0x7f4948165280>
name='langchain' id=UUID('8a5e8fff-93a4-49f3-8be7-5aac47cb3902') metadata=None
And I am calling like this
persist_directory=f'/home/hs/CustomBot/chroma-databases/{formatted_project_name}'
file=/home/hs/CustomBot/media/project/Code_of_Conduct_Policy.pdf
delete_document_embeddings_by_filename(file, persist_directory)
Still not able to delete embeddings of a pdf from the embeddings folder
### Suggestion:
_No response_
|
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| 1,976,612,484 |
PR_kwDOIPDwls5ejw60
| 12,845 |
CLI 0.0.14, Uvicorn update and no more [serve]
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[] | null | 1 | 2023-11-03T17:08:12 | 2023-11-03T18:05:54 | 2023-11-03T18:05:53 |
COLLABORATOR
| null |
Calls uvicorn directly from cli:
Reload works if you define app by import string instead of object.
(was doing subprocess in order to get reloading)
Version bump to 0.0.14
Remove the need for [serve] for simplicity.
Readmes are updated in #12847 to avoid cluttering this PR
|
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I_kwDOIPDwls51z-j1
| 12,843 |
vectorstores/docarray: `_similarity_search_with_relevance_scores` raises a NotImplementedError
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open
| false | null |
[] | null | 6 | 2023-11-03T16:33:14 | 2023-12-15T23:52:11 | null |
NONE
| null |
### System Info
Langchain 0.0.329
Python 3.10.11
MacOS 12.7.1 (21G920)
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
# Dependencies
import pathlib
from langchain.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores.docarray import DocArrayInMemorySearch
from langchain.llms.huggingface_pipeline import HuggingFacePipeline
from langchain import hub
# Setup
dir_data = pathlib.Path("../data_sample")
document_loader = DirectoryLoader(dir_data, show_progress=True)
documents = document_loader.load()
document_chunker = RecursiveCharacterTextSplitter(chunk_size=50, chunk_overlap=5)
document_chunks = document_chunker.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="multi-qa-MiniLM-L6-cos-v1")
vector_store = DocArrayInMemorySearch.from_documents(document_chunks, embeddings)
llm = HuggingFacePipeline.from_model_id(
task="text2text-generation",
model_id="google/flan-t5-small",
model_kwargs=dict(temperature=0.01, max_length=128, do_sample=True),
)
qa_rag_prompt = hub.pull("rlm/rag-prompt")
qa = RetrievalQA.from_chain_type(
llm,
retriever=vector_store.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.5}),
chain_type_kwargs={"prompt": qa_rag_prompt},
return_source_documents=True,
)
# OK: Supported by Vector Store (DocArrayInMemorySearch)
vector_store.similarity_search_with_score("What is the greatest ocean in the world?")
# NOK: NotImplemented @ `docarray.base`
answer = qa({"query": question}) # will fail
```
# Error Message
```
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores, normalized on a scale from 0 to 1.
0 is dissimilar, 1 is most similar.
"""
> raise NotImplementedError()
E NotImplementedError
venv/lib/python3.10/site-packages/langchain/vectorstores/docarray/base.py:140: NotImplementedError
```
### Expected behavior
The `{context}` variable provided to the prompt should only be _suffed_ by chunks retrieved with relevance score above the threshold.
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| 1,976,543,428 |
I_kwDOIPDwls51z6jE
| 12,842 |
Issue: Langchain ChatOpenAI chat_model.complete error message: 'chatopenai' object has no attribute 'complete'
|
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[] | null | 3 | 2023-11-03T16:26:06 | 2023-11-15T17:05:49 | 2023-11-15T17:05:49 |
NONE
| null |
I am new to Langchain and OpenAI models. I am creating a custom PDF reading in Python using Langchain ChatOpenAI model to interact with the chat completion endpoint. I keep getting this error message and I don't know what to do. Please help. All suggestions are welcome!
Here is a part of my code where the error message was generated.
# User Input
current_prompt = st.session_state.get('user_input', '')
prompt_placeholder = st.empty()
# Check if a submission has been made
if 'submitted' in st.session_state and st.session_state.submitted:
prompt = prompt_placeholder.text_area("Ask questions about your PDF:", value='', placeholder="Send a message", key="new_user_input")
else:
prompt = prompt_placeholder.text_area("Ask questions about your PDF:", value=current_prompt, placeholder="Send a message", key="user_input")
submit_button = st.button("Submit")
if submit_button and prompt:
# Indicate that a submission has been made
st.session_state.submitted = True
# Update the last input in session state
st.session_state.last_input = prompt
# Process user message
user_message = HumanMessage(content=prompt)
st.session_state.chat_history.append(user_message)
try:
# Similarity check
docs = VectorStore.similarity_search(query=prompt, k=3)
# Initialize chat model
chat_model = ChatOpenAI(model_name="gpt-3.5-turbo")
# Add a system message to the chat history to define the role
system_message = SystemMessage(content="You're a helpful assistant")
st.session_state.chat_history.append(system_message)
# Get a response from the chat model
completion_response = chat_model.complete(
messages=st.session_state.chat_history,
temperature=0.9 # Adjust temperature as needed
)
response_content = completion_response.choices[0].message['content']
# Process AI message using AIMessage
assistant_message = AIMessage(content=response_content)
st.session_state.chat_history.append(assistant_message)
# Load the question-answering chain
llm = OpenAI(model_name='gpt-3.5-turbo')
chain = load_qa_chain(llm=llm, chain_type="stuff")
# Run the question-answering chain with the documents and the user's prompt
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=prompt)
# This print statement is for debugging purposes
print(cb)
# st.write(response)
# st.write(docs)
# Append the QA chain response as an AI message
qa_response_message = AIMessage(content=response)
st.session_state.chat_history.append(qa_response_message)
except Exception as e:
st.error(f"An error occurred: {e}")
# Clear the input after processing
prompt_placeholder.text_area("Ask questions about your PDF:", value='', placeholder="Send a message", key="pdf_prompt")
# Save chat history
with open(chat_history_file, "wb") as f:
pickle.dump(st.session_state.chat_history, f)
# Display the entire chat
chat_content = ""
for message in st.session_state.chat_history:
if isinstance(message, HumanMessage):
role = "User" # Set the role manually for HumanMessage
content = message.content # Access the content attribute directly
elif isinstance(message, AIMessage):
role = "AI" # Set the role manually for AIMessage
content = message.content # Access the content attribute directly
else:
# Handle other types of messages or raise an error
role = "Unknown"
content = "Unsupported message type"
chat_content += f"<div style='background-color: #222222; color: white; padding: 10px;'>**{role}:** {content}</div>"
st.markdown(chat_content, unsafe_allow_html=True)
if __name__ == '__main__':
main()
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| 1,976,450,097 |
I_kwDOIPDwls51zjwx
| 12,841 |
`ChatAnyscale` not working because of OpenAI API Key
|
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open
| false | null |
[] | null | 7 | 2023-11-03T15:28:38 | 2023-11-11T04:34:52 | null |
NONE
| null |
### System Info
Langchain 0.0.329
Python 3.9
Windows 10
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import os
from langchain.chat_models import ChatAnyscale
os.environ['OPENAI_API_KEY'] = "mykey"
os.environ['ANYSCALE_API_KEY'] = "mykey"
ChatAnyscale()
```
```
95
96 def __init__(self, **kwargs: Any) -> None:
---> 97 super().__init__(**kwargs)
98 self._lc_kwargs = kwargs
99
/usr/local/lib/python3.10/dist-packages/pydantic/main.cpython-310-x86_64-linux-gnu.so in pydantic.main.BaseModel.__init__()
ValidationError: 1 validation error for ChatAnyscale
openai_api_key
str type expected (type=type_error.str)
```
### Expected behavior
It shouldn't return an error because my Keys are both correct. For example, `ChatOpenAI` works fine.
|
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| 1,976,320,267 |
I_kwDOIPDwls51zEEL
| 12,840 |
When saving an LLM to JSON, and retrieving it back the content_formatter is null
|
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[] | null | 2 | 2023-11-03T14:26:28 | 2023-11-03T14:35:26 | null |
CONTRIBUTOR
| null |
### System Info
langchain = 0.0.329
I have the following code, and it works fine as long as the JSON file does not exist.
```
try:
content_formatter = LlamaContentFormatter()
formatter_template = "Write a {word_count} word essay about {topic}."
prompt = PromptTemplate(
input_variables=["word_count", "topic"], template=formatter_template
)
try:
loaded_llm = load_llm("azureml.json")
chain = LLMChain(llm=loaded_llm, prompt=prompt)
except FileNotFoundError:
load_dotenv()
llm = AzureMLOnlineEndpoint(
endpoint_api_key=os.getenv("AZUREML_ENDPOINT_API_KEY"),
endpoint_url=os.getenv("AZUREML_ENDPOINT_URL"),
deployment_name=os.getenv("AZUREML_DEPLOYMENT_NAME"),
model_kwargs={"temperature": 0.8, "max_tokens": 300},
content_formatter=content_formatter
)
llm.save("azureml.json")
chain = LLMChain(llm=llm, prompt=prompt)
response = chain.run({"word_count": 100, "topic": "how to make friends"})
return response
except requests.exceptions.RequestException as e:
# Handle any requests-related errors (e.g., network issues, invalid URL)
raise ValueError(f"Error with the API request: {e}")
except json.JSONDecodeError as e:
# Handle any JSON decoding errors (e.g., invalid JSON format)
raise ValueError(f"Error decoding API response as JSON: {e}")
except Exception as e:
# Handle any other errors
raise ValueError(f"Error: {e}")
```
However when the llm is saved as azureml.json, the second time it loads the LLM from the file, however the second time I get this error:
ValueError: Error: 'NoneType' object has no attribute 'format_request_payload'
If I check the json file it looks like this:
```
{
"deployment_name": "llama-2-7b-12-luis",
"model_kwargs": {
"temperature": 0.8,
"max_tokens": 300
},
"_type": "azureml_endpoint"
}
```
I an guessing that the .save is not serializing the content formatter.
### 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
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Deploy Llama2 7B text generation as an azure managed endpoint.
2. Create a new project
3. Add .env file with these settings:
AZUREML_ENDPOINT_API_KEY=""
AZUREML_ENDPOINT_URL="https://<xxxx>.westeurope.inference.ml.azure.com/score"
AZUREML_DEPLOYMENT_NAME= "<xxxx>"
4. Add following code```
class LlamaContentFormatter(ContentFormatterBase):
"""Content formatter for LLaMa"""
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
"""Formats the request according the the chosen api"""
prompt = ContentFormatterBase.escape_special_characters(prompt)
request_payload = json.dumps(
{
"input_data": {
"input_string": [f'"{prompt}"'],
"parameters": model_kwargs,
}
}
)
return str.encode(request_payload)
def format_response_payload(self, output: bytes) -> str:
"""Formats response"""
return json.loads(output)[0]["0"]
def askdocuments(
question):
try:
content_formatter = LlamaContentFormatter()
formatter_template = "Write a {word_count} word essay about {topic}."
prompt = PromptTemplate(
input_variables=["word_count", "topic"], template=formatter_template
)
try:
loaded_llm = load_llm("azureml.json")
chain = LLMChain(llm=loaded_llm, prompt=prompt)
except FileNotFoundError:
load_dotenv()
llm = AzureMLOnlineEndpoint(
endpoint_api_key=os.getenv("AZUREML_ENDPOINT_API_KEY"),
endpoint_url=os.getenv("AZUREML_ENDPOINT_URL"),
deployment_name=os.getenv("AZUREML_DEPLOYMENT_NAME"),
model_kwargs={"temperature": 0.8, "max_tokens": 300},
content_formatter=content_formatter
)
llm.save("azureml.json")
chain = LLMChain(llm=llm, prompt=prompt)
response = chain.run({"word_count": 100, "topic": "how to make friends"})
return response
except requests.exceptions.RequestException as e:
# Handle any requests-related errors (e.g., network issues, invalid URL)
raise ValueError(f"Error with the API request: {e}")
except json.JSONDecodeError as e:
# Handle any JSON decoding errors (e.g., invalid JSON format)
raise ValueError(f"Error decoding API response as JSON: {e}")
except Exception as e:
# Handle any other errors
raise ValueError(f"Error: {e}")
def main():
add_company_logo_and_welcome_text()
st.markdown('#')
# Define a custom CSS class for the tooltip-like container
st.markdown(
"""
<style>
.tooltip-container {
position: relative;
display: inline-block;
cursor: pointer;
}
.tooltip-content {
visibility: hidden;
position: absolute;
background-color: #f9f9f9;
color: black;
padding: 5px;
border-radius: 3px;
z-index: 1;
top: -40px;
left: 100%;
width: 200px;
text-align: left;
white-space: normal;
}
.tooltip-container:hover .tooltip-content {
visibility: visible;
}
</style>
""",
unsafe_allow_html=True
)
# Create a radio button for user selection
selected_option = st.radio("Select an option:", ("langchain", "standard httprequest"))
# Store LLM generated responses
if "messages" not in st.session_state.keys():
st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.write(message["content"])
# User-provided prompt
if prompt := st.chat_input():
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.write(prompt)
# Generate a new response if last message is not from assistant
if st.session_state.messages[-1]["role"] != "assistant":
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
if selected_option == "langchain":
response = askdocuments(question=prompt)
st.write(response)
elif selected_option == "standard httprequest":
response = askdocuments2(question=prompt)
st.write(response)
message = {"role": "assistant", "content": response}
st.session_state.messages.append(message)
if __name__ == "__main__":
main()
```
### Expected behavior
no exceptions.
|
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PR_kwDOIPDwls5eidW0
| 12,839 |
coockbook: Fix typo in wikibase_agent.ipynb
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[] | null | 1 | 2023-11-03T13:34:05 | 2023-11-03T21:57:38 | 2023-11-03T21:57:37 |
CONTRIBUTOR
| null |
This patch fixes a spelling typo in message
within wikibase_agent.ipynb.
Signed-off-by: Masanari Iida <[email protected]>
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PR_kwDOIPDwls5eiYdA
| 12,838 |
update mongo template
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PR_kwDOIPDwls5eiRpF
| 12,837 |
In agent.stream() and agent.astream() yield actions before observations
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[] | null | 1 | 2023-11-03T12:59:57 | 2023-11-21T10:35:07 | 2023-11-21T10:35:06 |
COLLABORATOR
| null |
- This emits actions as soon as they are available, ie. right after plan
- When action is executed an ActionObservation object is emitted, which contains both the action and the return value of the tool
```
---- chunk 1
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})])]}
---- chunk 2
{'observations': [AgentObservation(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="Vittoria Ceretti's")]}
---- chunk 3
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Vittoria Ceretti's current age", log="\nInvoking: `Search` with `Vittoria Ceretti's current age`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti\'s current age"\n}'}})])]}
---- chunk 4
{'observations': [AgentObservation(action=AgentActionMessageLog(tool='Search', tool_input="Vittoria Ceretti's current age", log="\nInvoking: `Search` with `Vittoria Ceretti's current age`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti\'s current age"\n}'}})]), observation='25 years')]}
---- chunk 5
{'actions': [AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])]}
---- chunk 6
{'observations': [AgentObservation(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')]}
---- chunk 7
{'output': "Leo DiCaprio's current girlfriend is Vittoria Ceretti, and her age "
'(25 years) raised to the 0.43 power is approximately 3.99.'}
---- final
{'actions': [AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]),
AgentActionMessageLog(tool='Search', tool_input="Vittoria Ceretti's current age", log="\nInvoking: `Search` with `Vittoria Ceretti's current age`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti\'s current age"\n}'}})]),
AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})])],
'observations': [AgentObservation(action=AgentActionMessageLog(tool='Search', tool_input="Leo DiCaprio's current girlfriend", log="\nInvoking: `Search` with `Leo DiCaprio's current girlfriend`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Leo DiCaprio\'s current girlfriend"\n}'}})]), observation="Vittoria Ceretti's"),
AgentObservation(action=AgentActionMessageLog(tool='Search', tool_input="Vittoria Ceretti's current age", log="\nInvoking: `Search` with `Vittoria Ceretti's current age`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Search', 'arguments': '{\n "__arg1": "Vittoria Ceretti\'s current age"\n}'}})]), observation='25 years'),
AgentObservation(action=AgentActionMessageLog(tool='Calculator', tool_input='25^0.43', log='\nInvoking: `Calculator` with `25^0.43`\n\n\n', message_log=[AIMessageChunk(content='', additional_kwargs={'function_call': {'name': 'Calculator', 'arguments': '{\n "__arg1": "25^0.43"\n}'}})]), observation='Answer: 3.991298452658078')],
'output': "Leo DiCaprio's current girlfriend is Vittoria Ceretti, and her age "
'(25 years) raised to the 0.43 power is approximately 3.99.'}
```
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PR_kwDOIPDwls5eiImy
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issue #12165 mask API key in chat_models/azureml_endpoint module
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] | null | 1 | 2023-11-03T12:40:44 | 2023-11-10T19:05:19 | 2023-11-10T19:05:19 |
CONTRIBUTOR
| null |
- **Description:** `AzureMLChatOnlineEndpoint` object from langchain/chat_models/azureml_endpoint.py safe to print
without having any secrets included in raw format in the string representation.
- **Issue:** #12165,
- **Tag maintainer:** @eyurtsev
|
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| 1,976,111,325 |
I_kwDOIPDwls51yRDd
| 12,835 |
Issue: core dumped / segmentation fault
|
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| false | null |
[] | null | 2 | 2023-11-03T12:36:30 | 2023-11-03T12:45:14 | null |
NONE
| null |
### Issue you'd like to raise.
Hi,
I use debian 11,
python 3.10,
anaconda3
langchain==0.0.240
RAM = 16 Go
swap = 10 Go
SSD = 60 GO
I try to use the simplest program with Ctransfors:
```
from langchain.llms import CTransformers
from langchain.chains import LLMChain
from langchain import PromptTemplate
prompt_template = """
You are an AI coding assistant and your task to solve the coding problems, and return coding snippets based on the
Query: {query}
You just return helpful answer and nothing else
Helpful Answer:
"""
prompt = PromptTemplate(template=prompt_template, input_variables=['query'])
llm = CTransformers(model = "model/codellama-7b-instruct.ggmlv3.Q4_0.bin",
model_type = "llama",
max_new_tokens=512,
temperature=0.2
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
llm_response = llm_chain.run({"query": "Write a python code to load a CSV file using pandas library"})
print(llm_response)
```
and i have "core dumped " error without other details, i tried a lot of things but nothig resolve my problem !
can you help me please
thks
### Suggestion:
_No response_
|
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I_kwDOIPDwls51yO14
| 12,834 |
MultiRetrievalQAChain - unable to return source document when I use multiple documents
|
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[] | null | 2 | 2023-11-03T12:32:47 | 2023-11-03T12:45:31 | null |
NONE
| null |
### System Info
Python == 3.9.18
Langchain == 0.0.327
### Who can help?
MultiRetrievalQAChain
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
@hwchase17 @agol
Steps to reproduce behaviour:
1. Modified MultiRetrievalQAChain with source documents returned (added `return_source_documents=True`)
```
class MultiRetrievalQAChainAddSource(MultiRetrievalQAChain):
@classmethod
def from_retrievers(
cls,
llm: BaseLanguageModel,
retriever_infos: List[Dict[str, Any]],
default_retriever: Optional[BaseRetriever] = None,
default_prompt: Optional[PromptTemplate] = None,
default_chain: Optional[Chain] = None,
**kwargs: Any,
):
if default_prompt and not default_retriever:
raise ValueError(
"`default_retriever` must be specified if `default_prompt` is "
"provided. Received only `default_prompt`."
)
destinations = [f"{r['name']}: {r['description']}" for r in retriever_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_RETRIEVAL_ROUTER_TEMPLATE.format(destinations=destinations_str)
router_prompt = PromptTemplate(
template=router_template,
input_variables=["input"],
output_parser=RouterOutputParser(next_inputs_inner_key="query"),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt, return_source_documents=True)
destination_chains = {}
print('RETRIEVER_INFOSSSS', retriever_infos)
for r_info in retriever_infos:
prompt = r_info.get("prompt")
retriever = r_info["retriever"]
chain = RetrievalQA.from_llm(
llm, prompt=prompt, retriever=retriever, return_source_documents=True
)
name = r_info["name"]
destination_chains[name] = chain
print('USING DESTINATION CHAIN', destination_chains)
if default_chain:
_default_chain = default_chain
elif default_retriever:
_default_chain = RetrievalQA.from_llm(
llm, prompt=default_prompt, retriever=default_retriever, return_source_documents=True
)
else:
prompt_template = DEFAULT_TEMPLATE.replace("input", "query")
prompt = PromptTemplate(template=prompt_template, input_variables=["history", "query"])
_default_chain = ConversationChain(
llm=ChatOpenAI(), prompt=prompt, input_key="query", output_key="result" # type: ignore
)
return cls(
router_chain=router_chain,
destination_chains=destination_chains,
default_chain=_default_chain,
**kwargs,
)
```
2. create vectorstore (db = Weaviate(client,index_name,'text',attributes=['source'])
3. create retrievers (retriever = db.as_retriever())
4. retriever_infos for multiple retrievers:
```
chain = MultiRetrievalQAChainAddSource.from_retrievers(
llm=llm,
retriever_infos=retriever_infos,
verbose=True
)
```
5. using meditations.pdf and bushido.pdf as source documents
6. query for one retriever will return source docs (in this example - meditations.pdf):
``` "text": "json\n{\n \"destination\": \"meditations.pdf\",\n \"next_inputs\": \"What does Marcus Aurelius emphasize the most in Meditations?\"\n}\n", ```
```
'result': 'Marcus Aurelius emphasizes the importance of..., 'source_documents': [Document(page_content=\"Marcus Aurelius' Meditations - tr. Casaubon v. 8.16 , uploaded to www.philaletheians.co.uk , 14 July 2013 \\nPage 128 of 128 A parting thought\", metadata={'page': 127, 'source': 'meditations.pdf'}), Document(page_content=\"For it is not lawful , \\nthat anything that is of another and inferior kind and nature , be it what it will, as \\neither popular applause , or honour , or riches , or pleasures ; should be suffered to \\nconfront and contest as it were, with that which is rational , and operatively good. For \\nall these things , if once though but for a while , they begin to please , they presently \\nprevail , and pervert a man’s mind , or turn a man from the right way. Do thou there-\\nfore I say absolutely and freely make choice of that which is best, and stick unto it. \\nNow, that they say is best, which is most profitable . If they mean profitable to man \\nas he is a rational man, stand thou to it, and maintain it; but if they mean profitable , \\nas he is a creature , only reject it; and from this thy tenet and conclusion keep off \\ncarefully all plausible shows and colours of external appearance , that thou mayest \\nbe able to discern things rightly .\", metadata={'page': 24, 'source': 'meditations.pdf'})]}
```
6. query something for multiple retrievers
``` "text": "json\n{\n \"destination\": \"DEFAULT\",\n \"next_inputs\": \"marcus meditations and bushido similarities in life long learning and self improvement\"\n}\n ```
```
"result": "Both Marcus Meditations and Bushido emphasize the importance of life-long learning and self-improvement. In Marcus Aurelius' \"Meditations,\" he emphasizes the need for constant self-reflection and introspection as a means to improve oneself. He encourages individuals to examine their thoughts, actions, and values in order to develop a virtuous character.\n\nSimilarly, Bushido, which is the code of conduct followed by samurais in feudal Japan, also highlights the significance of continuous self-improvement. Bushido emphasizes the values of loyalty, honor, and discipline, and it encourages individuals to constantly strive for self-mastery in various aspects of life.\n\nBoth philosophies recognize that personal growth and development are ongoing processes that require self-discipline, reflection, and a commitment to learning. They emphasize the importance of cultivating virtues and values that contribute to a meaningful and fulfilling life.\n\nIt's fascinating to see how different cultures and historical periods have explored similar concepts of self-improvement and personal growth. Do you have any other questions or topics you'd like to discuss?
```
### Expected behavior
For single retriever, there is an output of the source documents. But for multiple retrievers, there is no output of the source documents (potentially due to ` \"destination\": \"DEFAULT\"` - just a hunch here)
For multiple retrievers:
```
"result": "Both Marcus Meditations and Bushido emphasize the importance of life-long learning and self-improvement. In Marcus Aurelius' \"Meditations,\" he emphasizes the need for constant self-reflection and introspection as a means to improve oneself. He encourages individuals to examine their thoughts, actions, and values in order to develop a virtuous character.\n\nSimilarly, Bushido, which is the code of conduct followed by samurais in feudal Japan, also highlights the significance of continuous self-improvement. Bushido emphasizes the values of loyalty, honor, and discipline, and it encourages individuals to constantly strive for self-mastery in various aspects of life.\n\nBoth philosophies recognize that personal growth and development are ongoing processes that require self-discipline, reflection, and a commitment to learning. They emphasize the importance of cultivating virtues and values that contribute to a meaningful and fulfilling life.\n\nIt's fascinating to see how different cultures and historical periods have explored similar concepts of self-improvement and personal growth. Do you have any other questions or topics you'd like to discuss?", 'source_documents': [Document(page_content=....", metadata={'page': 127, 'source': 'meditations.pdf'}), Document(page_content=...", metadata={'page': 24, 'source': 'meditations.pdf'}),Document(page_content=....", metadata={'page': ###, 'source': 'bushido.pdf'}), Document(page_content=...", metadata={'page': ###, 'source': 'bushido.pdf'})]}
```
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| 1,976,092,559 |
I_kwDOIPDwls51yMeP
| 12,833 |
Issue: Not able to delete embeddings of a file
|
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| false | null |
[] | null | 5 | 2023-11-03T12:28:42 | 2023-11-03T14:13:13 | null |
NONE
| null |
### Issue you'd like to raise.
def delete_document_embeddings_by_filename(file_name, persist_directory):
print(file_name,"file_name is ")
print(persist_directory,"persist_directory is")
chroma_db = chromadb.PersistentClient(path=persist_directory)
print(chroma_db,"chroma_db is ---------------->>>>>>>>>>")
collection = chroma_db.get_collection(name="langchain")
metadata_list = collection.get()['metadatas']
print(metadata_list)
file_names = []
for metadata in metadata_list:
filename = metadata['source'].split('\\')[-1]
if filename not in file_names:
file_names.append(filename)
print(file_names,"file_names is kkkkkkkkkkkkkkkkkkkkkkk")
print(collection,"collection is ---------------->>>>>>>>>>")
print("hello world ------------------------>>>>>>>>>>>>>>>>>>")
print(collection.get(where={"source": file_name})['ids'])
collection.delete(where={"source": file_name})
I got print(collection.get(where={"source": file_name})['ids']) as empty list and collection object as name='langchain' id=UUID('8a5e8fff-93a4-49f3-8be7-5aac47cb3902') metadata=None
also I got print(collection.get(where={"source": file_name})['ids']) as empty list
### Suggestion:
_No response_
|
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I_kwDOIPDwls51yKUI
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RecursiveCharacterTextSplitter with regex separator raises error if there is only 1 match of the regex
|
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[] | null | 3 | 2023-11-03T12:25:13 | 2023-11-03T13:22:30 | null |
NONE
| null |
### System Info
- Langchain=0.0.325
- Python=3.10.0
- Windows
### Who can help?
@IlyaMichlin @hwchase17 @baskaryan
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
Running this example:
`from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema.document import Document
splitter = RecursiveCharacterTextSplitter(
chunk_size=100,
chunk_overlap=20,
length_function = len,
is_separator_regex = True,
separators=["\n(?=\d.\d{1,2}(\.*)\n)", "\.(?=\n\w)", "\n", "\.", " "]
)
documents = [Document(page_content="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum. Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. \n1.1\nNeque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?")]
splitted_documents = splitter.split_documents(documents)
print(splitted_documents)
`
Raises 'list index out of range' error on the line "splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)]" of the text_splitter.py file
`def _split_text_with_regex(
text: str, separator: str, keep_separator: bool
) -> List[str]:
# Now that we have the separator, split the text
if separator:
if keep_separator:
# The parentheses in the pattern keep the delimiters in the result.
_splits = re.split(f"({separator})", text)
splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)]
if len(_splits) % 2 == 0:
splits += _splits[-1:]
splits = [_splits[0]] + splits
else:
splits = re.split(separator, text)
else:
splits = list(text)
return [s for s in splits if s != ""]`
### Expected behavior
The expected behavior was not to raise the error, but continue with the recursive split by the next patterns.
|
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| 1,976,042,143 |
PR_kwDOIPDwls5eh2LC
| 12,831 |
fix AgentTokenBufferMemory to properly honour token limits
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[] | null | 2 | 2023-11-03T12:03:37 | 2023-11-23T22:46:20 | null |
NONE
| null |
- **Description:**
AgentBufferTokenMemory was not properly reding existing messages from history and going over set token limit. This PR changes its implementation slightly by keeping separate, in-memory, buffer that holds recent messages whose total size does not exceed specified max_token_limit. Chat history object holds and retains all messages.
- **Issue:** the issue #
Oveflowing chat memory and therefore token limit budget for llm calls
- **Dependencies:**
None
- **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
- **Twitter handle:**
NA
Note to self:
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @hwchase17.
|
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PR_kwDOIPDwls5ehrIu
| 12,830 |
Fix agent async timeout handling and Add async tests for agent
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[] | null | 1 | 2023-11-03T11:34:20 | 2023-11-21T10:40:06 | 2023-11-21T10:40:05 |
COLLABORATOR
| null |
It was raising a TimeoutError instead of emitting timeout output
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc:
https://github.com/langchain-ai/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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| 1,975,991,138 |
PR_kwDOIPDwls5ehqzo
| 12,829 |
Update s3_directory.py
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[] | null | 3 | 2023-11-03T11:33:35 | 2023-12-05T10:51:10 | null |
CONTRIBUTOR
| null |
read all the files inside the sub-directory of the given bucket or given prefix .
since the files are getting loaded via unstructured loader
a fix of issue #11854
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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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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PR_kwDOIPDwls5ehmrU
| 12,828 |
fix bes vector store search
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[] | null | 1 | 2023-11-03T11:22:40 | 2023-11-06T10:16:50 | 2023-11-03T22:39:19 |
CONTRIBUTOR
| null |
**Issue:**
fix search body in baidu cloud vectorsearch
|
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PR_kwDOIPDwls5ehlA7
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template: use dashes instead of underscores for neo4j-cypher package and path in readme
|
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[] | null | 1 | 2023-11-03T11:18:11 | 2023-11-03T22:54:48 | 2023-11-03T22:54:48 |
CONTRIBUTOR
| null |
Minimal readme template update
underscores didn't work, dashes do
|
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I_kwDOIPDwls51xlPh
| 12,826 |
Issue: Is langchain automatically adjusting its prompts to account for required formats of models such as llama2?
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[] | null | 2 | 2023-11-03T10:56:15 | 2023-11-03T11:15:57 | null |
NONE
| null |
### Issue you'd like to raise.
I am wondering if I need to adjust the prompt template of the combine_docs_chain (combine_docs_chain.llm_chain.prompt.template) to account for the needs of llama 2 models that require the following format:
[INST] <<SYS>> You are a HR-assistant that answers questions:
<</SYS>>
{query} [/INST]
### Suggestion:
_No response_
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I_kwDOIPDwls51xMAL
| 12,825 |
Extraction of contents from docx files through UnstructuredWordDocumentLoader
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[] | null | 17 | 2023-11-03T10:03:17 | 2023-11-15T10:28:34 | null |
NONE
| null |
### System Info
langchain=0.0.208
platform=Windows
python=3.9.16
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.document_loaders import UnstructuredWordDocumentLoader
loader = UnstructuredWordDocumentLoader(docx_file_path, mode="elements")
data = loader.load()
data
### Expected behavior
Page numbers of the contents extracted using UnstructuredWordDocumentLoader(docx_file_path, mode="elements") are not in sync with the actual page number of the contents which are there in the docx file.
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I_kwDOIPDwls51xE7U
| 12,824 |
Request to add OpenSearchVectorSearch delete index method
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[] | null | 0 | 2023-11-03T09:43:28 | 2023-11-03T09:43:47 | null |
NONE
| null |
### Feature request
In OpenSearchVectorSearch langchain wrapper there is no way to delete the indexes from vector DB.
### Motivation
I was working on a project and had to delete some indexes because they already existed in DB.
Had to write the code myself to do that.
### Your contribution
Yes I can create a PR for the issue.
|
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I_kwDOIPDwls51w1oz
| 12,823 |
Issue: rephrasing of question in Conversational retrieval chain which delays responses and users see it
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[] | null | 2 | 2023-11-03T09:10:55 | 2023-11-03T09:24:47 | null |
NONE
| null |
### Issue you'd like to raise.
When you give a query, it rephrases the query(which isn't the issue), but the rephrased query gets shown which is very problematic because it delays response and the rephrase gets shown to the user.
example, if I ask What is an apple is, i get shown something like " What is the definition of an apple
Getting the rephrased question delays the response to obtain the first token, and this rephrased question gets shown to the user first before the real answer comes out, which isn't good at all
I want a way where it can rephrase the question, but it doesn't need to increase response time and it doesn't need to show it
### Suggestion:
_No response_
|
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PR_kwDOIPDwls5egm8D
| 12,822 |
Chroma vectorstore get_ids
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[] | null | 3 | 2023-11-03T08:36:47 | 2023-11-21T14:40:07 | null |
CONTRIBUTOR
| null |
Simply added a get_ids method, that returns a list of all ids in the chroma vectorstore.
To dynamically add, delete and update documents in a vectorstore you need to know which ids are in the vectorstore. With this function, it's just a bit easier to access them.
Thought about creating an abstract method in the Vectorstore interface. So it's available per default. But I don't know how to access the ids of the other vectorstore types.
@jarib
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I_kwDOIPDwls51wEej
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Unable to read OTP from an email whose content is in form of HTML/Image
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[] | null | 2 | 2023-11-03T06:34:28 | 2023-11-03T06:46:26 | null |
NONE
| null |
### Issue you'd like to raise.
I want to read otp using gmail tool kit but I am getting an errror when the email is in form of HTML or in image form .
How do I fix this ?
### Suggestion:
_No response_
|
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| 12,819 |
Template Readmes and Standardization
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[] | null | 1 | 2023-11-03T05:04:54 | 2023-11-03T20:15:30 | 2023-11-03T20:15:29 |
COLLABORATOR
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I_kwDOIPDwls51vqqw
| 12,818 |
[Chat template] Chat LangChain
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[] | null | 1 | 2023-11-03T04:14:37 | 2023-11-03T04:15:16 | null |
COLLABORATOR
| null |
### Feature request
Move Chat [LangChain](https://github.com/langchain-ai/chat-langchain) into a template.
### Motivation
ChatLangchain has useful features (prompts, etc) that should be captured in a templates.
### Your contribution
Add template
|
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PR_kwDOIPDwls5efrz3
| 12,817 |
Initialize HuggingFacePipeline parameters when transformers pipeline passed in directly
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[] | null | 4 | 2023-11-03T03:42:02 | 2023-12-06T08:04:33 | null |
CONTRIBUTOR
| null |
When a transformers pipeline is passed directly into `HuggingFacePipeline`, it always sets `model_id` to 'gpt2' no matter what model has been loaded into the pipeline.
To replicate the problem:
```python
model_id = "Salesforce/codegen-350M-mono"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10, temperature=0.3, top_p=0.95)
hf = HuggingFacePipeline(pipeline=pipe)
print(hf)
print(hf.pipeline.model.name_or_path)
```
`print(hf)` returns
```python
HuggingFacePipeline
Params: {'model_id': 'gpt2', 'model_kwargs': None, 'pipeline_kwargs': None}
```
despite the pipeline model having correctly loaded `Salesforce/codegen-350M-mono`.
This is confusing and can lead to folks assuming that the pipeline model has been loaded incorrectly.
Additionally, `model_id` and `kwargs` are convenient parameters to use when initialized
This issue was reported in #3451 and #8280
This PR addresses this by initializing the `HuggingFacePipeline` parameters whenever a pipeline object is passed directly.
After this change, using the above example:
```python
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10, temperature=0.3, top_p=0.95)
hf = HuggingFacePipeline(pipeline=pipe)
print(hf)
```
returns
```python
HuggingFacePipeline
Params: {'model_id': 'Salesforce/codegen-350M-mono', 'model_kwargs': None, 'pipeline_kwargs': {'max_new_tokens': 10, 'temperature': 0.3, 'top_p': 0.95}}
```
<!-- Thank you for contributing to LangChain!
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|
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| 1,975,400,042 |
PR_kwDOIPDwls5efqIs
| 12,816 |
Add Dataset Page to printout
|
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[] | null | 1 | 2023-11-03T03:28:17 | 2023-11-03T03:36:57 | 2023-11-03T03:36:57 |
COLLABORATOR
| null | null |
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| 1,975,379,949 |
I_kwDOIPDwls51veft
| 12,815 |
DOC: docs/expression_language/cookbook/tools --next leads to a previous page
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[] | null | 4 | 2023-11-03T02:53:58 | 2023-11-03T04:21:01 | null |
NONE
| null |
### Issue with current documentation:
Converting https://python.langchain.com/ to a pdf using https://github.com/jean-humann/docs-to-pdf runs forever because https://python.langchain.com/docs/expression_language/cookbook/tools --next leads to a previous page.
`npx docs-to-pdf --initialDocURLs="https://python.langchain.com/" --contentSelector="article" --paginationSelector="a.pagination-nav__link.pagination-nav__link--next" --coverImage="https://upload.wikimedia.org/wikipedia/commons/3/3f/LangChain_logo.png" --coverTitle="LangChain"`
### Idea or request for content:
_No response_
|
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| 12,814 |
Autopopulate module name in cli init
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| 1,975,362,686 |
PR_kwDOIPDwls5efiRE
| 12,813 |
Free knowledge base pod information update
|
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[] | null | 1 | 2023-11-03T02:24:17 | 2023-11-16T09:32:42 | 2023-11-13T07:22:43 |
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/langchain-ai/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.
-->
We updated MyScale free knowledge base, where you can try your RAG with 36 million paragraphs from wikipedia and 2 million paragraphs from ArXiv.
The pod has two tables
```sql
CREATE TABLE default.ChatArXiv (
`abstract` String,
`id` String,
`vector` Array(Float32),
`metadata` Object('JSON'),
`pubdate` DateTime,
`title` String,
`categories` Array(String),
`authors` Array(String),
`comment` String,
`primary_category` String,
VECTOR INDEX vec_idx vector TYPE MSTG('metric_type=Cosine'),
CONSTRAINT vec_len CHECK length(vector) = 768)
ENGINE = ReplacingMergeTree ORDER BY id;
CREATE TABLE wiki.Wikipedia (
`id` String,
`title` String,
`text` String,
`url` String,
`wiki_id` UInt64,
`views` Float32,
`paragraph_id` UInt64,
`langs` UInt32,
`emb` Array(Float32),
VECTOR INDEX emb_idx emb TYPE MSTG('metric_type=Cosine'),
CONSTRAINT emb_len CHECK length(emb) = 768)
ENGINE = ReplacingMergeTree ORDER BY id;
```
You can connect those two tables using credentials below (just the same to the old one)
URL: `msc-4a9e710a.us-east-1.aws.staging.myscale.cloud`
Port: `443`
Username: `chatdata`
Password: `myscale_rocks`
It's FREE and you can also use it with
ChatData: https://github.com/myscale/ChatData
Retrieval-QA-Benchmark: https://github.com/myscale/Retrieval-QA-Benchmark
... and also LangChain!
Request for review @baskaryan
|
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| 1,975,246,193 |
I_kwDOIPDwls51u91x
| 12,812 |
Langchain ConfluenceLoader.load(): Get "when" field in metadata
|
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open
| false | null |
[] | null | 2 | 2023-11-03T00:26:16 | 2023-11-03T00:39:28 | null |
NONE
| null |
### System Info
langchain: 0.0.311
### 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
- [X] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
We're trying to load confluence pages using `ConfluenceLoader.load()`.
If we load pages by list of page_ids:
```
loader = ConfluenceLoader(
url=baseUrl,
username=username,
api_key= api_key
)
documents = loader.load(
page_ids = ["2081227164", "2096890841", "2081227310"],
include_attachments=False,
limit=50,
max_pages=1000
)
```
We are able to get `when` field in metadata.
However, if we reuqest pages by space
```
documents = loader.load(
space_key = spaceName,
include_attachments=False,
limit=50,
max_pages=1000
)
```
The when field will not be in metadata.
### Expected behavior
Is there any methods we can request pages by space and also get `when` field? This is really important for us.
Thanks :)
|
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PR_kwDOIPDwls5efIT5
| 12,811 |
Bagatur/tiledb
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| false | null |
[] | null | 1 | 2023-11-03T00:24:34 | 2023-11-03T00:40:33 | 2023-11-03T00:40:32 |
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/langchain-ai/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_kwDOIPDwls5efHmL
| 12,810 |
migrate
|
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| false | null |
[] | null | 1 | 2023-11-03T00:21:34 | 2023-11-16T09:32:41 | 2023-11-03T00:56:27 |
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/langchain-ai/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_kwDOIPDwls5ee5FW
| 12,809 |
Update template index
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[] | null | 1 | 2023-11-02T23:04:17 | 2023-11-03T00:51:41 | 2023-11-03T00:51:40 |
COLLABORATOR
| null | null |
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PR_kwDOIPDwls5eeywe
| 12,808 |
Adds missing `urllib.parse` for IDE warning of `PubMedAPIWrapper`
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[] | null | 1 | 2023-11-02T22:33:51 | 2023-11-03T00:36:25 | 2023-11-03T00:27:25 |
CONTRIBUTOR
| null |
Resolves an IDE (PyCharm 2023.2.3 PE) warning around `urllib.parse.quote`, also enabling CTRL-click
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