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] | open | false | null | [] | null | 1 | 2024-01-03T08:12:56 | 2024-01-03T08:21:03 | null | NONE | null | ### Issue you'd like to raise.
Hello Team,
let me clearify the issue below:
we are using vectordb conversational retriever. we can look into the attached code for more clarity. My issue is i have 4 documents stored in 2 different index. **End-user can select multiple document lets say 3 or 4 document, and ask questions related to any of the selected 4 document, he dont care whatever the document he selects its there on same index.** As a backend logic we can create something index document map by some logic which will give a map of documents existing in different index.
**Now the problem here is i need to pass all the index at once where this 3-4 documents are there, there may be possibility we get more than 2 index where the selected document exist.**
is there a way to pass multi-index while search into below function. i see index accept string argument. let me know if you suggest any workaround for this. we need solution on some better workaround then we are using.
**Below workaround we are using:**
we are creating one temp index on the go and get all the embeddings and text from all the index where the selected document exist, then use that index with query and in the same request we delete that temp index.
Let me know in case you need any other additional details
### Suggestion:
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https://api.github.com/repos/langchain-ai/langchain/issues/15457 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15457/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15457/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15457/events | https://github.com/langchain-ai/langchain/issues/15457 | 2,063,449,745 | I_kwDOIPDwls56_b6R | 15,457 | Langchain SQL Database Agent failed to find the view name in the MS SQL database. | {
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] | open | false | null | [] | null | 5 | 2024-01-03T08:03:31 | 2024-01-03T13:03:15 | null | NONE | null | I am trying to build a langchain SQL database agent where I want to query only one view for now. I have mentioned the view name in the System Prompt and I have passed view_support=True to the SQLDatabase constructor. When I run the query agent is trying to find out the tables instead views. I am sensing that agent has no method that can fetch the view names of the database, it is trying to call 'sql_db_list_tables' and then sql_db_schema.
look at the code below:
```
import os
from dotenv import load_dotenv
load_dotenv()
from sqlalchemy import create_engine
odbc_str = 'mssql+pyodbc:///?odbc_connect=' \
'Driver={ODBC Driver 18 for SQL Server}' \
';Server=tcp:' + os.getenv("DB_SERVER")+';PORT=1453' + \
';Authentication=ActiveDirectoryPassword' + \
';DATABASE=' + os.getenv("DB_NAME") + \
';Uid='+os.getenv("UID")'+ \
';Pwd='+os.getenv("PWD")'+ \
';Encrypt=yes;TrustServerCertificate=no;Connection Timeout=30;'
print(odbc_str)
db_engine = create_engine(odbc_str)
```
```
from langchain.chat_models import AzureChatOpenAI
llm = AzureChatOpenAI(
openai_api_type=os.getenv("OPENAI_API_TYPE"),
api_version=os.getenv("OPENAI_API_VERSION"),
azure_deployment=os.getenv("DEPLOYMENT_NAME"),
model=os.getenv("OPENAI_CHAT_MODEL"),
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_API_KEY"),
temperature=0,
verbose= True)
```
```
from langchain.prompts.chat import ChatPromptTemplate
final_prompt = ChatPromptTemplate.from_messages(
[
("system",
"""
You are a helpful AI assistant expert in querying SQL Database to
find answers to user's question about database view: XXXXXXXXXX.vw_YYYYYYYYY
where XXXXXXXXXX is the schema and vw_YYYYYYYYY is the view. You need to directly query
this view: XXXXXXXXXX.vw_YYYYYYYYY for all user's questions.
Note: DO NOT EXECUTE DROP, DELETE or UPDATE SQL QUERIES.
"""
),
("user", "{question}\n ai: "),
]
)
```
```
from langchain.agents import AgentType, create_sql_agent
from langchain.sql_database import SQLDatabase
from langchain.agents.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
db = SQLDatabase(db_engine, view_support=True)
sql_toolkit = SQLDatabaseToolkit(db=db, llm=llm)
sql_toolkit.get_tools()
sqldb_agent = create_sql_agent(
llm=llm,
toolkit=sql_toolkit,
agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
)
sqldb_agent.run(final_prompt.format(
question="What is the Exposure for id: 574182 by type ?"
))
```
AGENT ANSWER:
> Entering new AgentExecutor chain...
Action: sql_db_list_tables
Action Input: ""
Observation: xyz, abc, pqr
Thought:The table I need to query, XXXXXXXXXX.vw_YYYYYYYYY, is not listed in the database tables. I need to check the schema of the view to understand its structure and the fields it contains.
Action: sql_db_schema
Action Input: "XXXXXXXXXX.vw_YYYYYYYYY"
Observation: Error: table_names {'XXXXXXXXXX.vw_YYYYYYYYY'} not found in database
Thought:It seems like there is an error in finding the view XXXXXXXXXX.vw_YYYYYYYYY in the database. I need to inform the user about this.
Final Answer: I'm sorry, but it seems like the view 'XXXXXXXXXX.vw_YYYYYYYYY' does not exist in the database. Please check the view name and try again.
> Finished chain.
One option is to override the sql agent action methods or add new methods, but I dont want to do do that as per prompt instructions it should successfully find the view in the schema and query.
Please help anyone has achieved this. Thanks in advance. | {
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] | open | false | null | [] | null | 1 | 2024-01-03T07:30:33 | 2024-01-03T07:44:13 | null | NONE | null | ### Issue you'd like to raise.
Hi, I want to know if there is an implementation to use ConversationChains, Agents with NVIDIA's Nemo Rails.
Thanks
### Suggestion:
_No response_ | {
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] | open | false | null | [] | null | 1 | 2024-01-03T07:22:33 | 2024-01-03T07:23:45 | null | CONTRIBUTOR | null | fix https://github.com/langchain-ai/langchain/issues/15203
Or maybe can delete the render.py file directly | {
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] | open | false | null | [] | null | 2 | 2024-01-03T07:17:37 | 2024-01-08T19:53:07 | null | CONTRIBUTOR | null | **Description:** Allow binding parameters in queries run by SQLDatabase
**Issue:** https://github.com/langchain-ai/langchain/issues/15449
**Dependencies:** N/A
**Twitter handle:** N/A
Let me know if you need me to write tests, it looks like this class doesn't have any tests. | {
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@efriis | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15449 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15449/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15449/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15449/events | https://github.com/langchain-ai/langchain/issues/15449 | 2,063,266,287 | I_kwDOIPDwls56-vHv | 15,449 | Allow bind variables in SQLDatabase queries | {
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] | open | false | null | [] | null | 1 | 2024-01-03T05:26:19 | 2024-01-03T05:28:36 | null | CONTRIBUTOR | null | ### Feature request
Allow using [bind parameters](https://docs.sqlalchemy.org/en/20/core/connections.html#sqlalchemy.engine.Connection.execute.params.parameters) in SQLDatabase's [run method](https://github.com/langchain-ai/langchain/blob/65afc13b8b53a1ca41a1a3998dad9eb8d83ca917/libs/community/langchain_community/utilities/sql_database.py#L426).
### Motivation
I don't see a way to use bind params in SQLDatabase queries.
### Your contribution
Happy to help out with a PR if needed (and if confirmed that this functionality doesn't exist and is wanted :) ) | {
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] | open | false | null | [] | null | 3 | 2024-01-03T04:03:31 | 2024-01-03T06:10:14 | null | NONE | null | ### System Info
langchain 0.0.353
langchain-community 0.0.7
langchain-core 0.1.4
langchain-experimental 0.0.47
Python 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] on linuxd
openai 0.28.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```python
import os
from langchain.llms import AzureOpenAI
from langchain.agents import initialize_agent, AgentType, AgentExecutor
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate, ChatPromptTemplate, MessagesPlaceholder
from langchain.tools import tool
import openai
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["OPENAI_API_VERSION"] = "2023-06-01-preview"
os.environ["OPENAI_API_BASE"] = "xxx"
os.environ["OPENAI_API_KEY"] = "xxx"
prompt_template = ChatPromptTemplate.from_messages([
("system", "You are a powerful assistant. But you do not know how to calculate the length of a word"),
("user", "{input}"),
])
llm = AzureOpenAI(
model_name="gpt-4",
engine="gpt-4"
)
@tool
def get_word_length(word):
"""get the length of a word"""
return len(word)
tools = [get_word_length]
agent = initialize_agent(
tools,
llm,
agent=AgentType.OPENAI_FUNCTIONS,
verbose=True
)
agent.run("how many letters are there in Oslo and Beijing?")
```
```
> Entering new AgentExecutor chain...
Traceback (most recent call last):
File "/mnt/workspace/workgroup/lengmou/Tars-Code-Agent/pages/5.py", line 115, in <module>
agent.run("how many letters are there in Oslo and Beijing?")
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/chains/base.py", line 507, in run
return self(args[0], callbacks=callbacks, tags=tags, metadata=metadata)[
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/chains/base.py", line 312, in __call__
raise e
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/chains/base.py", line 306, in __call__
self._call(inputs, run_manager=run_manager)
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/agents/agent.py", line 1312, in _call
next_step_output = self._take_next_step(
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/agents/agent.py", line 1038, in _take_next_step
[
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/agents/agent.py", line 1038, in <listcomp>
[
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/agents/agent.py", line 1066, in _iter_next_step
output = self.agent.plan(
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py", line 96, in plan
predicted_message = self.llm.predict_messages(
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain_core/language_models/llms.py", line 946, in predict_messages
content = self(text, stop=_stop, **kwargs)
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain_core/language_models/llms.py", line 892, in __call__
self.generate(
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain_core/language_models/llms.py", line 666, in generate
output = self._generate_helper(
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain_core/language_models/llms.py", line 553, in _generate_helper
raise e
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain_core/language_models/llms.py", line 540, in _generate_helper
self._generate(
File "/mnt/workspace/workgroup/shared/miniconda3/envs/code-agent/lib/python3.10/site-packages/langchain_community/llms/openai.py", line 1152, in _generate
"token_usage": full_response["usage"],
KeyError: 'usage'
```
### Expected behavior
Please look at this issue, thanks! | {
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] | open | false | null | [] | null | 1 | 2024-01-03T03:29:06 | 2024-01-03T03:36:40 | null | NONE | null | ### Issue you'd like to raise.
Hi, I'm using an llm chain and would like to stream its output. I'm writing a function that takes it's token. I can get the tokens one by one, but how can I know if this token is the last token of the response?
### Suggestion:
_No response_ | {
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- **Issue:** no issue,
- **Dependencies:** not change any dependencies | {
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] | closed | false | null | [] | null | 1 | 2024-01-03T02:58:01 | 2024-01-05T04:07:25 | 2024-01-05T04:07:25 | CONTRIBUTOR | null | ### Feature request
If in-memory replica is increased in milvus, `replica_number` should be set when loading collection in langchain, but the default setting is 1 and cannot be changed.
> pymilvus.exceptions.MilvusException: <MilvusException: (code=1100, message=failed to load collection: can't change the replica number for loaded collection: expected=3, actual=1: invalid parameter)>
When creating the Milvus object, please change it so that `replica_number` can be passed over to the argument
### Motivation
There is no way to change `replica_number` in langchain when in-memory replica is increased in milvus.
### Your contribution
- `milvus.py`
```python3
def __init__(
self,
embedding_function: Embeddings,
collection_name: str = "LangChainCollection",
connection_args: Optional[dict[str, Any]] = None,
consistency_level: str = "Session",
index_params: Optional[dict] = None,
search_params: Optional[dict] = None,
drop_old: Optional[bool] = False,
*,
primary_field: str = "pk",
text_field: str = "text",
vector_field: str = "vector",
replica_number : int = 1 // added factor
):
"""Initialize the Milvus vector store."""
try:
from pymilvus import Collection, utility
except ImportError:
raise ValueError(
"Could not import pymilvus python package. "
"Please install it with `pip install pymilvus`."
)
# Default search params when one is not provided.
self.default_search_params = {
"IVF_FLAT": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_SQ8": {"metric_type": "L2", "params": {"nprobe": 10}},
"IVF_PQ": {"metric_type": "L2", "params": {"nprobe": 10}},
"HNSW": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_FLAT": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_SQ": {"metric_type": "L2", "params": {"ef": 10}},
"RHNSW_PQ": {"metric_type": "L2", "params": {"ef": 10}},
"IVF_HNSW": {"metric_type": "L2", "params": {"nprobe": 10, "ef": 10}},
"ANNOY": {"metric_type": "L2", "params": {"search_k": 10}},
"AUTOINDEX": {"metric_type": "L2", "params": {}},
}
self.embedding_func = embedding_function
self.collection_name = collection_name
self.index_params = index_params
self.search_params = search_params
self.consistency_level = consistency_level
# In order for a collection to be compatible, pk needs to be auto'id and int
self._primary_field = primary_field
# In order for compatibility, the text field will need to be called "text"
self._text_field = text_field
# In order for compatibility, the vector field needs to be called "vector"
self._vector_field = vector_field
self.fields: list[str] = []
# Create the connection to the server
if connection_args is None:
connection_args = DEFAULT_MILVUS_CONNECTION
self.alias = self._create_connection_alias(connection_args)
self.col: Optional[Collection] = None
# Grab the existing collection if it exists
if utility.has_collection(self.collection_name, using=self.alias):
self.col = Collection(
self.collection_name,
using=self.alias,
)
# If need to drop old, drop it
if drop_old and isinstance(self.col, Collection):
self.col.drop()
self.col = None
# Initialize the vector store
self._init(replica_number=replica_number)
```
Please let me take the `replica_number` as an argument in the same way as above | {
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- **Twitter handle:** None
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https://api.github.com/repos/langchain-ai/langchain/issues/15437 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15437/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15437/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15437/events | https://github.com/langchain-ai/langchain/pull/15437 | 2,063,088,998 | PR_kwDOIPDwls5jFnCk | 15,437 | refactored `memory` | {
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] | open | false | null | [] | null | 2 | 2024-01-03T00:07:53 | 2024-01-17T19:01:05 | null | COLLABORATOR | null | Created `langchain_community.memory`. Moved there two classes from `langchain.memory`
This movement triggered another changes:
1.
There is a lint rule that `langchain_community` code should not depend on `langchain`. Below, I show these dependencies:
```
`ZepMemory`
--> langchain.memory.buffer.`ConversationBufferMemory`
--> langchain.memory.chat_memory.`BaseChatMemory`
--> langchain_community.chat_message_histories.in_memory.`ChatMessageHistory`
--> langchain.memory.utils.`get_prompt_input_key` # the same as in MotorheadMemory
`MotorheadMemory`
--> langchain.memory.chat_memory.BaseChatMemory # then the same dependency as in ZepMemory
--> langchain_community.chat_message_histories.in_memory.ChatMessageHistory
--> langchain.memory.utils.get_prompt_input_key
So, these classes and a function were moved into `core` because of the above dependencies:
- `ConversationBufferMemory`
- `BaseChatMemory`
- `ChatMessageHistory`
- `get_prompt_input_key`
```
2.
Then the next lint error forced a change in `experimental`:
mkdir -p .mypy_cache; poetry run mypy langchain_experimental --cache-dir .mypy_cache
langchain_experimental/autonomous_agents/autogpt/memory.py:3: error: Module "langchain.memory.chat_memory" has no attribute "get_prompt_input_key" [attr-defined]
-- this was a bug in langchain_experimental/autonomous_agents/autogpt/memory.py:3 (wrong import)
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https://api.github.com/repos/langchain-ai/langchain/issues/15435 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15435/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15435/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15435/events | https://github.com/langchain-ai/langchain/pull/15435 | 2,063,080,467 | PR_kwDOIPDwls5jFlRi | 15,435 | docs: together ai updates | {
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- moved `ernie_functions` artifacts to `community` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15431 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15431/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15431/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15431/events | https://github.com/langchain-ai/langchain/issues/15431 | 2,062,998,345 | I_kwDOIPDwls569ttJ | 15,431 | Accessing Gemini though Vertex AI SDK | {
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] | closed | false | null | [] | null | 3 | 2024-01-02T21:52:47 | 2024-01-03T17:20:09 | 2024-01-03T17:20:09 | NONE | null | ### Feature request
Gemini API is not available in Canada, but i believe it is available through `vertexai.preview.generative_models` in pre-GA mode.
Would it be possible to add a feature using the Vertex AI SDK instead of Gemini API, which i assume is what it is using?
### Motivation
Canada access to Gemini through Langchain
### Your contribution
I could test? | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15430 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15430/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15430/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15430/events | https://github.com/langchain-ai/langchain/pull/15430 | 2,062,982,778 | PR_kwDOIPDwls5jFQzT | 15,430 | add new chain howtos | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15429 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15429/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15429/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15429/events | https://github.com/langchain-ai/langchain/pull/15429 | 2,062,979,107 | PR_kwDOIPDwls5jFQBc | 15,429 | docs, experimenta[patch], langchain[patch], community[patch]: update storage imports | {
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] | null | 1 | 2024-01-02T21:31:11 | 2024-01-02T21:47:12 | 2024-01-02T21:47:11 | COLLABORATOR | null | ran
```bash
g grep -l "langchain.vectorstores" | xargs -L 1 sed -i '' "s/langchain\.vectorstores/langchain_community.vectorstores/g"
g grep -l "langchain.document_loaders" | xargs -L 1 sed -i '' "s/langchain\.document_loaders/langchain_community.document_loaders/g"
g grep -l "langchain.chat_loaders" | xargs -L 1 sed -i '' "s/langchain\.chat_loaders/langchain_community.chat_loaders/g"
g grep -l "langchain.document_transformers" | xargs -L 1 sed -i '' "s/langchain\.document_transformers/langchain_community.document_transformers/g"
g grep -l "langchain\.graphs" | xargs -L 1 sed -i '' "s/langchain\.graphs/langchain_community.graphs/g"
g grep -l "langchain\.memory\.chat_message_histories" | xargs -L 1 sed -i '' "s/langchain\.memory\.chat_message_histories/langchain_community.chat_message_histories/g"
gco master libs/langchain/tests/unit_tests/*/test_imports.py
gco master libs/langchain/tests/unit_tests/**/test_public_api.py
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15428 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15428/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15428/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15428/events | https://github.com/langchain-ai/langchain/pull/15428 | 2,062,970,911 | PR_kwDOIPDwls5jFORS | 15,428 | added fix for key error: doc_id | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T21:22:35 | 2024-01-02T22:59:54 | 2024-01-02T22:59:54 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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https://api.github.com/repos/langchain-ai/langchain/issues/15427 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15427/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15427/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15427/events | https://github.com/langchain-ai/langchain/pull/15427 | 2,062,921,492 | PR_kwDOIPDwls5jFDUs | 15,427 | core:adds tests for partial_variables | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15426 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15426/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15426/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15426/events | https://github.com/langchain-ai/langchain/issues/15426 | 2,062,806,705 | I_kwDOIPDwls568-6x | 15,426 | FakeStreamingListLLM.astream() yields strings while ChatOpenAI yields AIMessageChunk | {
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```
langchain==0.0.352
langchain-community==0.0.6
langchain-core==0.1.3
```
Python `3.12.1` running inside Docker from `python:3.12-bookworm` on Linux.
### Who can help?
I have a FastAPI app that streams the output of an LLM. The app uses `langchain.chat_models.ChatOpenAI` at runtime, but during test I mock the LLM with `langchain.llms.fake.FakeStreamingListLLM`. However, when my app calls `.astream()` on each of them I'm getting different results:
- While `ChatOpenAI` yields instances of `AIMessageChunk`...
- `FakeStreamingListLLM` yields instances of strings.
This mismatch between the fake and the real class makes the fake unsuitable for some mocking purposes as the interface of `AIMessageChunk` is different from a `str`.
@baskaryan @agola11
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [X] Async
### Reproduction
See an snippet below on how to reproduce the output of strings within `ipython`:
```python
from langchain.llms.fake import FakeStreamingListLLM
import asyncio
async def pp(llm):
async for chunk in llm.astream('some input'):
yield(f"{type(chunk)}: {chunk}")
async def consume():
results = []
async for item in pp(FakeStreamingListLLM(responses=['a'])):
results.append(item)
return results
asyncio.run(consume())
# OUTPUT:
["<class 'str'>: a"]
```
### Expected behavior
I'd expect `FakeStreamingListLLM` to return instances of `AIMessageChunk` as the real chat model. | {
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- infra: fail check_diffs if too many files changed
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Checking that it does what I expect in #15424 | {
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- **Issue:** This fixes [Issue #14663](https://github.com/langchain-ai/langchain/issues/14663)
- **Twitter handle:** [@consultchrisq ](https://twitter.com/consultchrisq?lang=en)
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But github only checks first 300 files: https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#git-diff-comparisons
> Diffs are limited to 300 files. If there are files changed that aren't matched in the first 300 files returned by the filter, the workflow will not run. You may need to create more specific filters so that the workflow will run automatically. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15417 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15417/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15417/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15417/events | https://github.com/langchain-ai/langchain/issues/15417 | 2,062,736,735 | I_kwDOIPDwls568t1f | 15,417 | Filter conditions are discarded when using multiple filter conditions in similarity_search_with_relevance_scores | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T17:35:24 | 2024-01-04T15:22:37 | 2024-01-04T15:22:37 | NONE | null | ### System Info
Windows 10
Python 3.11.5
langchain==0.0.331
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I want to do a similarity search on a vectorstore created beforehand and use multiple filtering conditions. The relevant part of the code is as follows:
```
from langchain.vectorstores import Chroma
from langchain.embeddings import SentenceTransformerEmbeddings
import chromadb
db_path = "my_db"
embeddings = SentenceTransformerEmbeddings(cache_folder='intfloat/multilingual-e5-large')
chroma_client = chromadb.PersistentClient(path=db_path)
db= Chroma(persist_directory=db_path, embedding_function=embeddings, client=chroma_client)
query = "My query"
filtered = db.similarity_search_with_relevance_scores(k=5, query=query, filter={"key1":value1, "key2":value2})
```
When using one filtering condition like
```
filtered = db.similarity_search_with_relevance_scores(k=5, query=query, filter={"key1":value1})
```
the filtering condtion is applied and it works fine. But when using multiple conditions, neither of the conditions is applied.
How can I use multiple filtering conditions?
### Expected behavior
I would expect, that both conditions are applied either connected by an "and" or an "or". | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15412 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15412/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15412/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15412/events | https://github.com/langchain-ai/langchain/pull/15412 | 2,062,655,902 | PR_kwDOIPDwls5jEKpO | 15,412 | docs, community[patch], experimental[patch], langchain[patch], cli[pa… | {
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] | null | 1 | 2024-01-02T16:29:11 | 2024-01-02T20:32:18 | 2024-01-02T20:32:17 | COLLABORATOR | null | …tch]: import models from community
ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
``` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15411 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15411/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15411/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15411/events | https://github.com/langchain-ai/langchain/pull/15411 | 2,062,655,660 | PR_kwDOIPDwls5jEKl- | 15,411 | community: integrate with model Yuan2.0 | {
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] | open | false | null | [] | null | 6 | 2024-01-02T16:28:57 | 2024-01-16T06:37:26 | null | NONE | null | 1. integrate with [`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. update `langchain.llms`
3. add a new doc for [Yuan2.0 integration](docs/docs/integrations/llms/yuan2.ipynb) | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15410 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15410/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15410/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15410/events | https://github.com/langchain-ai/langchain/pull/15410 | 2,062,621,162 | PR_kwDOIPDwls5jEDIk | 15,410 | langchain[patch], experimental[patch]: replace langchain.schema imports | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T16:02:54 | 2024-01-02T20:09:46 | 2024-01-02T20:09:46 | COLLABORATOR | null | Import from core instead.
Ran:
```bash
git grep -l 'from langchain.schema\.output_parser' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.output_parser/from\ langchain_core.output_parsers/g"
git grep -l 'from langchain.schema\.messages' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.messages/from\ langchain_core.messages/g"
git grep -l 'from langchain.schema\.document' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.document/from\ langchain_core.documents/g"
git grep -l 'from langchain.schema\.runnable' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.runnable/from\ langchain_core.runnables/g"
git grep -l 'from langchain.schema\.vectorstore' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.vectorstore/from\ langchain_core.vectorstores/g"
git grep -l 'from langchain.schema\.language_model' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.language_model/from\ langchain_core.language_models/g"
git grep -l 'from langchain.schema\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.embeddings/from\ langchain_core.embeddings/g"
git grep -l 'from langchain.schema\.storage' | xargs -L 1 sed -i '' "s/from\ langchain\.schema\.storage/from\ langchain_core.stores/g"
git checkout master libs/langchain/tests/unit_tests/schema/
make format
cd libs/experimental
make format
cd ../langchain
make format
```
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https://api.github.com/repos/langchain-ai/langchain/issues/15408 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15408/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15408/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15408/events | https://github.com/langchain-ai/langchain/pull/15408 | 2,062,597,518 | PR_kwDOIPDwls5jD-D_ | 15,408 | Community: BedrockChat -> Support Titan express as chat model | {
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] | open | false | null | [] | null | 2 | 2024-01-02T15:43:33 | 2024-01-15T20:03:15 | null | CONTRIBUTOR | null | Titan Express model was not supported as a chat model because LangChain messages were not "translated" to a text prompt.
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] | closed | false | null | [] | null | 7 | 2024-01-02T15:33:53 | 2024-01-03T19:31:42 | 2024-01-03T19:31:42 | NONE | null | ### Issue you'd like to raise.
Hi there,
I am creating a simple PDF reading application where I want to push my embeddings into Pinecone.
Here is my code:
## Imports
import streamlit as st
import os
from apikey import apikey
import pickle
from PyPDF2 import PdfReader
# Streamlit - user interface
from streamlit_extras.add_vertical_space import add_vertical_space
# Langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.llms import OpenAI
from langchain.chains.question_answering import load_qa_chain
from langchain.callbacks import get_openai_callback
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import (SystemMessage, HumanMessage, AIMessage)
from langchain.document_loaders import UnstructuredPDFLoader
# Pinecone
from langchain.vectorstores import Pinecone
import pinecone
from apikey import pinecone_api_key
import uuid
os.environ['OPENAI_API_KEY'] = apikey
## User Interface
# Side Bar
with st.sidebar:
st.title('🚀 Zi-GPT Version 2.0')
st.markdown('''
## About
This app is an LLM-powered chatbot built using:
- [Streamlit](https://streamlit.io/)
- [LangChain](https://python.langchain.com/)
- [OpenAI](https://platform.openai.com/docs/models) LLM model
''')
add_vertical_space(5)
st.write('Made with ❤️ by Zi')
# Main Page
def main():
st.header("Zi's PDF Helper: Chat with PDF")
# upload a PDF file
pdf = st.file_uploader("Please upload your PDF here", type='pdf')
# st.write(pdf)
# read PDF
if pdf is not None:
pdf_reader = PdfReader(pdf)
# data = pdf_reader.load()
# split document into chunks
# also can use text split: good for PDFs that do not contains charts and visuals
sections = []
for page in pdf_reader.pages:
# Split the page text by paragraphs (assuming two newlines indicate a new paragraph)
page_sections = page.extract_text().split('\n\n')
sections.extend(page_sections)
chunks = sections
# st.write(chunks)
# text_splitter = RecursiveCharacterTextSplitter(
# chunk_size = 500,
# chunk_overlap = 20
# )
# chunks = text_splitter.split_documents(data)
## embeddings
# Set up embeddings
embeddings = OpenAIEmbeddings( model="text-embedding-ada-002")
# Set up Pinecone
pinecone.init(api_key=pinecone_api_key, environment='gcp-starter')
index_name = 'langchainresearch'
if index_name not in pinecone.list_indexes():
pinecone.create_index(index_name, dimension=1536, metric="cosine") # Adjust the dimension as per your embeddings
index = pinecone.Index(index_name)
docsearch = Pinecone.from_documents(chunks, embeddings, index_name = index_name)
# Create or Load Chat History
if pdf:
# generate chat history
chat_history_file = f"{pdf.name}_chat_history.pkl"
# load history if exist
if os.path.exists(chat_history_file):
with open(chat_history_file, "rb") as f:
chat_history = pickle.load(f)
else:
chat_history = []
# Initialize chat_history in session_state if not present
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
# Check if 'prompt' is in session state
if 'last_input' not in st.session_state:
st.session_state.last_input = ''
# User Input
current_prompt = st.session_state.get('user_input', '')
prompt_placeholder = st.empty()
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:
# Update the last input in session state
st.session_state.last_input = prompt
docs = docsearch.similarity_search(query=prompt, k=3)
#llm = OpenAI(temperature=0.9, model_name='gpt-3.5-turbo')
chat = ChatOpenAI(model='gpt-4', temperature=0.7, max_tokens=3000)
message = [
SystemMessage(content="You are a helpful assistant"),
HumanMessage(content=prompt)
]
chain = load_qa_chain(llm=chat, chain_type="stuff")
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=message)
print(cb)
# st.write(response)
# st.write(docs)
# Process the response using AIMessage schema
# ai_message = AIMessage(content="AI message content")
# ai_message.content = response.generations[0].message.content
# Add to chat history
st.session_state.chat_history.append((prompt, response))
# Save chat history
with open(chat_history_file, "wb") as f:
pickle.dump(st.session_state.chat_history, f)
# Clear the input after processing
prompt_placeholder.text_area("Ask questions about your PDF:", value='', placeholder="Send a message", key="pdf_prompt")
# Display the entire chat
chat_content = ""
for user_msg, bot_resp in st.session_state.chat_history:
chat_content += f"<div style='background-color: #222222; color: white; padding: 10px;'>**You:** {user_msg}</div>"
chat_content += f"<div style='background-color: #333333; color: white; padding: 10px;'>**Zi GPT:** {bot_resp}</div>"
st.markdown(chat_content, unsafe_allow_html=True)
if __name__ == '__main__':
main()
After running the code I got this error message that I cannot find a solution.
AttributeError: 'str' object has no attribute 'page_content'
Traceback:
File "C:\Users\zy73\AppData\Roaming\Python\Python311\site-packages\streamlit\runtime\scriptrunner\script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "C:\Users\zy73\OneDrive\Desktop\AI Research\langchain\pdf.py", line 159, in <module>
main()
File "C:\Users\zy73\OneDrive\Desktop\AI Research\langchain\pdf.py", line 88, in main
docsearch = Pinecone.from_documents(chunks, embeddings, index_name = index_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zy73\AppData\Roaming\Python\Python311\site-packages\langchain\schema\vectorstore.py", line 508, in from_documents
texts = [d.page_content for d in documents]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zy73\AppData\Roaming\Python\Python311\site-packages\langchain\schema\vectorstore.py", line 508, in <listcomp>
texts = [d.page_content for d in documents]
Please help me debug this. Thank you
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15406 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15406/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15406/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15406/events | https://github.com/langchain-ai/langchain/pull/15406 | 2,062,545,582 | PR_kwDOIPDwls5jDy4J | 15,406 | Feature request: Provide exception objects in fallback | {
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] | closed | false | null | [] | null | 3 | 2024-01-02T15:03:32 | 2024-01-17T14:43:43 | 2024-01-17T14:43:43 | NONE | null | ## Motivation
I have an increasingly complex chain of LLM calls, and I would like to be able to continue it even if some intermediate requests fail. For example, if a prompt requires 10 requests as input and 1 of them fails, I might just use an empty string for it and continue with the remaining 9 as input. The [fallbacks](https://python.langchain.com/docs/guides/fallbacks) mechanism seems to work well for this:
```python
chain.with_fallbacks([RunnableLambda(lambda input: "")])
```
However, currently the exception objects are not exposed to the fallback runnable. You can only specify the exception *types* to catch by using the `exceptions_to_handle=` option.
For example, say one of the requests produces the following error:
```python
BadRequestError('Error code: 400 - {\'error\': {\'message\': "This model\'s maximum context length is 16385 tokens. However, your messages resulted in 17351 tokens. Please reduce the length of the messages.", \'type\': \'invalid_request_error\', \'param\': \'messages\', \'code\': \'context_length_exceeded\'}}')
```
I'd like to be able to surface this in the UI, so that I know *why* the request is failing, and that it's not just a transient server error – even though I want the chain to continue executing with missing data.
## Suggested implementation
One solution I can see is changing `RunnableWithFallbacks` so that it passes the exception object into the `configurable` dictionary in the [config](https://python.langchain.com/docs/expression_language/how_to/configure) of the fallback runnables. This way you can obtain the exception like this:
```python
from langchain_core.runnables import RunnableLambda, RunnableConfig
def handle_error(input, config: RunnableConfig):
exception = config['configurable']['exception']
# Store the `exception` object somewhere so it can be surfaced to the user
return ''
chain = chain.with_fallbacks([RunnableLambda(handle_error)])
```
I'm attaching a patch that implements this in the Python library. I've only tested the `ainvoke` change in my code. In particular, I don't know if code for the `batch`/`abatch` case is correct. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15404 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15404/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15404/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15404/events | https://github.com/langchain-ai/langchain/issues/15404 | 2,062,463,881 | I_kwDOIPDwls567rOJ | 15,404 | How to get the complete output as answer | {
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] | open | false | null | [] | null | 3 | 2024-01-02T14:01:51 | 2024-01-03T10:21:19 | null | NONE | null | ### System Info
python: 3.11
langchain:latest
### Who can help?
in an chatbot after running a query it will return the SQLResult, but while giving an output answer complete result is not displayed
code:
import pandas as pd
import sqlalchemy as sal
import os, sys, openai
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
# import ChatOpenAI
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
from langchain import PromptTemplate
# from langchain.prompts.PromptTemplate import PromptTemplate
# from langchain.models import ChatGPTClient
# from langchain.utils import save_conversation
os.environ['OPENAI_API_KEY'] = openapi_key
def chat(question):
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
# custom_suffix = """""
# If the SQLResult is empty, the Answer should be "No results found". DO NOT hallucinate an answer if there is no result."""
engine = create_engine(connection_uri)
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
# db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
from langchain.prompts import PromptTemplate
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the SQLResult as answer.
The question: {db_chain.run}
"""
prompt_template = """" Use the following pieces of context to answer the question at the end.
If you don't know the answer, please think rationally and answer from your own knowledge base.
Don't consider the table which are not mentioned, if no result is matching with the keyword Please return the answer as invalid question
{context}
Question: {questions}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "questions"]
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
return db_chain.run(question)
answer= chat("Aadhaar number of B#########")
print(answer)
result:
> Entering new SQLDatabaseChain chain...
Aadhaar number of Bhuvaneshwari
SQLQuery:SELECT [Aadhaar Number]
FROM [EGV_emp_departments_ChatGPT]
WHERE [EmployeeName] = 'B##########'
SQLResult: [('91#######',), ('71#########',)]
Answer:91######
> Finished chain.
9########
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
in an chatbot after running a query it will return the SQLResult, but while giving an output answer complete result is not displayed
code:
import pandas as pd
import sqlalchemy as sal
import os, sys, openai
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
# import ChatOpenAI
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
from langchain import PromptTemplate
# from langchain.prompts.PromptTemplate import PromptTemplate
# from langchain.models import ChatGPTClient
# from langchain.utils import save_conversation
os.environ['OPENAI_API_KEY'] = openapi_key
def chat(question):
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
# custom_suffix = """""
# If the SQLResult is empty, the Answer should be "No results found". DO NOT hallucinate an answer if there is no result."""
engine = create_engine(connection_uri)
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
# db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
from langchain.prompts import PromptTemplate
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the SQLResult as answer.
The question: {db_chain.run}
"""
prompt_template = """" Use the following pieces of context to answer the question at the end.
If you don't know the answer, please think rationally and answer from your own knowledge base.
Don't consider the table which are not mentioned, if no result is matching with the keyword Please return the answer as invalid question
{context}
Question: {questions}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "questions"]
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
return db_chain.run(question)
answer= chat("Aadhaar number of B#########")
print(answer)
result:
> Entering new SQLDatabaseChain chain...
Aadhaar number of Bhuvaneshwari
SQLQuery:SELECT [Aadhaar Number]
FROM [EGV_emp_departments_ChatGPT]
WHERE [EmployeeName] = 'B##########'
SQLResult: [('91#######',), ('71#########',)]
Answer:91######
> Finished chain.
9########
### Expected behavior
in an chatbot after running a query it will return the SQLResult, but while giving an output answer complete result is not displayed
code:
import pandas as pd
import sqlalchemy as sal
import os, sys, openai
import constants
from langchain.sql_database import SQLDatabase
from langchain.llms.openai import OpenAI
from langchain_experimental.sql import SQLDatabaseChain
from sqlalchemy import create_engine
# import ChatOpenAI
from langchain.chat_models import ChatOpenAI
from typing import List, Optional
from langchain.agents.agent_toolkits import SQLDatabaseToolkit
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.chat_models import ChatOpenAI
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain.sql_database import SQLDatabase
from langchain.text_splitter import TokenTextSplitter
from langchain.tools import BaseTool
from langchain.tools.sql_database.tool import QuerySQLDataBaseTool
from secret_key import openapi_key
from langchain import PromptTemplate
# from langchain.prompts.PromptTemplate import PromptTemplate
# from langchain.models import ChatGPTClient
# from langchain.utils import save_conversation
os.environ['OPENAI_API_KEY'] = openapi_key
def chat(question):
from urllib.parse import quote_plus
server_name = constants.server_name
database_name = constants.database_name
username = constants.username
password = constants.password
encoded_password = quote_plus(password)
connection_uri = f"mssql+pyodbc://{username}:{encoded_password}@{server_name}/{database_name}?driver=ODBC+Driver+17+for+SQL+Server"
# custom_suffix = """""
# If the SQLResult is empty, the Answer should be "No results found". DO NOT hallucinate an answer if there is no result."""
engine = create_engine(connection_uri)
model_name="gpt-3.5-turbo-16k"
db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments_ChatGPT'])
# db = SQLDatabase(engine, view_support=True, include_tables=['EGV_emp_departments'])
llm = ChatOpenAI(temperature=0, verbose=False, model=model_name)
db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)
from langchain.prompts import PromptTemplate
PROMPT = """
Given an input question, first create a syntactically correct mssql query to run,
then look at the results of the query and return the SQLResult as answer.
The question: {db_chain.run}
"""
prompt_template = """" Use the following pieces of context to answer the question at the end.
If you don't know the answer, please think rationally and answer from your own knowledge base.
Don't consider the table which are not mentioned, if no result is matching with the keyword Please return the answer as invalid question
{context}
Question: {questions}
"""
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "questions"]
)
def split_text(text, chunk_size, chunk_overlap=0):
text_splitter = TokenTextSplitter(
chunk_size=chunk_size, chunk_overlap=chunk_overlap
)
yield from text_splitter.split_text(text)
class QuerySQLDatabaseTool2(QuerySQLDataBaseTool):
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
result = self.db.run_no_throw(query)
return next(split_text(result, chunk_size=14_000))
class SQLDatabaseToolkit2(SQLDatabaseToolkit):
def get_tools(self) -> List[BaseTool]:
tools = super().get_tools()
original_query_tool_description = tools[0].description
new_query_tool = QuerySQLDatabaseTool2(
db=self.db, description=original_query_tool_description
)
tools[0] = new_query_tool
return tools
return db_chain.run(question)
answer= chat("Aadhaar number of B#########")
print(answer)
result:
> Entering new SQLDatabaseChain chain...
Aadhaar number of Bhuvaneshwari
SQLQuery:SELECT [Aadhaar Number]
FROM [EGV_emp_departments_ChatGPT]
WHERE [EmployeeName] = 'B##########'
SQLResult: [('91#######',), ('71#########',)]
Answer:91######
> Finished chain.
9########
instead of a single output or top5 output, need the complete result from SQLResults as answe. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15403 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15403/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15403/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15403/events | https://github.com/langchain-ai/langchain/issues/15403 | 2,062,455,269 | I_kwDOIPDwls567pHl | 15,403 | Issue: not able to replicate documentation results | {
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] | open | false | null | [] | null | 1 | 2024-01-02T13:55:08 | 2024-01-02T14:04:43 | null | NONE | null | ### Issue you'd like to raise.
Hi! I am trying to replicate this tutorial https://python.langchain.com/docs/integrations/toolkits/playwright on Colab using the same code, only difference is i am using `ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)` instead of `ChatAnthropic(temperature=0)`.
When i run the agent (`result = await agent_chain.arun("What are the headers on langchain.com?")`) instead of obtaining the full output showed in the documentation, i get:
> Entering new AgentExecutor chain...
I'll need to extract the headers from the langchain.com website. Let me do that for you.
Action:```navigate_browser```
> Finished chain.
I'll need to extract the headers from the langchain.com website. Let me do that for you.
Action:```navigate_browser```.
It seems the agent stops before returning the final answer, any guess on why this is happening? Thanks a lot
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15402 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15402/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15402/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15402/events | https://github.com/langchain-ai/langchain/pull/15402 | 2,062,433,191 | PR_kwDOIPDwls5jDaqc | 15,402 | docs: show last update time | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15400 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15400/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15400/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15400/events | https://github.com/langchain-ai/langchain/issues/15400 | 2,062,195,571 | I_kwDOIPDwls566ptz | 15,400 | Warning message- No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction || Chroma db | {
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] | open | false | null | [] | null | 1 | 2024-01-02T10:09:11 | 2024-01-02T10:11:29 | null | NONE | null | ### System Info
langchain - 0.0.350
Python - 3.11
chromadb - 0.3.23
OS - Win 10
### Who can help?
@hwchase17
@eyur
### 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
just updated the version from 0.0.330 to 350
used the same code to create a chromadb class -
and when using Chroma.from_docs(docs, embeddings) get this in output -
No embedding_function provided, using default embedding function: SentenceTransformerEmbeddingFunction
### Expected behavior
The expected behavior shall be that the embedding function provided should be used to create the embeddings,
The following is the fix for the issue -
line 129 should have a embedding_function passed instead of None | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15399 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15399/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15399/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15399/events | https://github.com/langchain-ai/langchain/issues/15399 | 2,062,178,490 | I_kwDOIPDwls566li6 | 15,399 | DOC: Resolve URL Navigation Issues - Trailing Slash Discrepancy | {
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] | open | false | null | [] | null | 1 | 2024-01-02T09:54:31 | 2024-01-02T09:56:46 | null | NONE | null | ### Issue with current documentation:
Links within the documentation fail to open when the URL lacks a trailing '/'. Notably, the links on the page indicated in the example URL below and other links across various pages result in `page not found` errors when the URL lacks a trailing '/'. It's crucial to clarify that both URLs below, with and without a trailing '/', point to the same page. The issue lies in the fact that links on the version without '/' do not open, giving a `page not found` error, whereas the version with '/' functions correctly without any error.
Example URL (without '/' at the end of the URL): [https://python.langchain.com/docs/modules/agents](https://python.langchain.com/docs/modules/agents)
Example URL (with '/' at the end of the URL): [https://python.langchain.com/docs/modules/agents/](https://python.langchain.com/docs/modules/agents/)
### Idea or request for content:
- **Suggested Changes:**
1. Investigate and address the issue causing links to fail when the URL lacks a trailing '/'.
2. Implement a fix to ensure all links across the documentation open successfully, regardless of the presence or absence of a trailing '/' in the URL.
- **Steps to Reproduce:**
1. Navigate to [Example URL without '/'](https://python.langchain.com/docs/modules/agents) in a web browser.
2. Attempt to click on various links within the documentation, especially those that navigate to other pages.
3. Observe the page not found errors when the URL lacks a trailing '/'.
- **Additional Context:**
- This issue is consistent across multiple pages in the documentation.
- The problem is observed on Chrome, Brave, Firefox, and Bing browsers.
- Other browsers may also have this issue, but I haven't tested in them. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15398 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15398/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15398/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15398/events | https://github.com/langchain-ai/langchain/pull/15398 | 2,062,152,350 | PR_kwDOIPDwls5jCeK4 | 15,398 | langchain: convert json to minified json to save prompt token cost | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T09:31:14 | 2024-01-03T03:02:39 | 2024-01-03T03:02:39 | CONTRIBUTOR | null | - **Description:** convert json to minified json to save prompt token cost,
- **Issue:** no issue,
- **Dependencies:** not change any dependencies,
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https://api.github.com/repos/langchain-ai/langchain/issues/15397 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15397/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15397/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15397/events | https://github.com/langchain-ai/langchain/pull/15397 | 2,062,126,192 | PR_kwDOIPDwls5jCYhd | 15,397 | Skypoint v0.0.351 | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T09:07:04 | 2024-01-02T09:13:20 | 2024-01-02T09:07:23 | NONE | null | Merge version 0.0.351 to skypoint-langchain | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15396 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15396/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15396/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15396/events | https://github.com/langchain-ai/langchain/issues/15396 | 2,062,114,468 | I_kwDOIPDwls566V6k | 15,396 | 这个貌似是一个bug， 修改之后就好了 | {
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] | open | false | null | [] | null | 1 | 2024-01-02T08:55:07 | 2024-01-03T02:51:03 | null | NONE | null | ### System Info
/Users/sunwenke/miniconda3/envs/langchain/bin/python /Users/sunwenke/workspace/yongxinApi/langchain/localopenai/sql.py
Traceback (most recent call last):
File "/Users/sunwenke/workspace/yongxinApi/langchain/localopenai/sql.py", line 8, in <module>
entity_store = SQLiteEntityStore(db_file="/Users/sunwenke/workspace/yongxinApi/langchain/Chinook.db")
File "/Users/sunwenke/miniconda3/envs/langchain/lib/python3.10/site-packages/langchain/memory/entity.py", line 256, in __init__
self.conn = sqlite3.connect(db_file)
File "/Users/sunwenke/miniconda3/envs/langchain/lib/python3.10/site-packages/pydantic/v1/main.py", line 357, in __setattr__
raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"')
ValueError: "SQLiteEntityStore" object has no field "conn"
### 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
- [x] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
from langchain.memory import ConversationEntityMemory
from langchain.memory.entity import SQLiteEntityStore
from langchain.memory.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from localopenai import llm
entity_store = SQLiteEntityStore(db_file="/Users/sunwenke/workspace/yongxinApi/langchain/Chinook.db")
memory = ConversationEntityMemory(llm=llm, entity_store=entity_store)
conversation = ConversationChain(
llm=llm,
prompt=ENTITY_MEMORY_CONVERSATION_TEMPLATE,
memory=memory,
verbose=True,
)
conversation.run("有多少员工")
### Expected behavior
class SQLiteEntityStore(BaseEntityStore):
"""SQLite-backed Entity store"""
session_id: str = "default"
table_name: str = "memory_store"
def __init__(
self,
session_id: str = "default",
db_file: str = "entities.db",
table_name: str = "memory_store",
*args: Any,
**kwargs: Any,
):
try:
import sqlite3
except ImportError:
raise ImportError(
"Could not import sqlite3 python package. "
"Please install it with `pip install sqlite3`."
)
super().__init__(*args, **kwargs)
self.conn = sqlite3.connect(db_file)
self.session_id = session_id
self.table_name = table_name
self._create_table_if_not_exists()
self.conn = sqlite3.connect(db_file) 属性不存在 需要 在当前类 添加conn属性 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15395 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15395/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15395/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15395/events | https://github.com/langchain-ai/langchain/issues/15395 | 2,062,113,700 | I_kwDOIPDwls566Vuk | 15,395 | Issue: Unable to use SQLDatabaseChain with Falcon 7b Instruct for quering the postgresql database. | {
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] | open | false | null | [] | null | 1 | 2024-01-02T08:54:23 | 2024-01-02T09:03:53 | null | NONE | null | ### Issue you'd like to raise.
Hi Langchain Gurus,
I am trying to use SQLDatabaseChain to query and answer questions on postgresql table. so far the code that I have written uses following hugging face pipeline
```
model_name ='tiiuae/falcon-7b-instruct'
tokenizer = AutoTokenizer.from_pretrained(model_name)
pipe = pipeline("question-answering", model=model_name, tokenizer=tokenizer, torch_dtype=bfloat16)
llm = HuggingFacePipeline(pipeline=pipe)
```
the prompt template looks like following
```
default_prompt = """You are a postgresql expert. Given an input question, first create a syntactically correct postgresql query to run, then look at the results of the query and return the answer to the input question.
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per postgresql. You can order the results to return the most informative data in the database.
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
Pay attention to use date('now') function to get the current date, if the question involves "today".
Use the following format:
Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here
Only use the following tables:
{table_info}
Question: {question}
"""
```
post that I am using the SQLDatabaseChain like this
```
database = SQLDatabase.from_uri(f"postgresql+psycopg2://{db_user}:{db_password}@{db_host}:5432/{db_name}")
prompt_template = PromptTemplate.from_template(prompt_template)
print("SQLDatabase loaded.")
db_chain = SQLDatabaseChain.from_llm(llm, self.database, verbose=True)
db_chain.run('How many records are available in flightData table')
```
running this throws error
```
``
Use the following format:
Question: Question here
SQLQuery: SQL Query to run
SQLResult: Result of the SQLQuery
Answer: Final answer here
Only use the following tables:
CREATE TABLE "FlightData" (
year BIGINT,
month BIGINT,
day BIGINT,
dep_time DOUBLE PRECISION,
sched_dep_time BIGINT,
dep_delay DOUBLE PRECISION,
arr_time DOUBLE PRECISION,
sched_arr_time BIGINT,
arr_delay DOUBLE PRECISION,
carrier TEXT,
flight BIGINT,
tailnum TEXT,
origin TEXT,
dest TEXT,
air_time DOUBLE PRECISION,
distance BIGINT,
hour BIGINT,
minute BIGINT,
time_hour TEXT
)
/*
3 rows from FlightData table:
year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier flight tailnum origin dest air_time distance hour minute time_hour
2013 1 1 517.0 515 2.0 830.0 819 11.0 UA 1545 N14228 EWR IAH 227.0 1400 5 15 1/1/2013 5:00
2013 1 1 533.0 529 4.0 850.0 830 20.0 UA 1714 N24211 LGA IAH 227.0 1416 5 29 1/1/2013 5:00
2013 1 1 542.0 540 2.0 923.0 850 33.0 AA 1141 N619AA JFK MIA 160.0 1089 5 40 1/1/2013 5:00
*/
Question: How many records are available in flightData table
SQLQuery: argument needs to be of type (SquadExample, dict)
```
Requesting help /pointer on how I can I run this code without error and generate the correct answer?
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15394 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15394/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15394/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15394/events | https://github.com/langchain-ai/langchain/issues/15394 | 2,062,050,508 | I_kwDOIPDwls566GTM | 15,394 | similarity_search get 2 validation errors for DocArrayDoc | {
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] | open | false | null | [] | null | 2 | 2024-01-02T07:44:12 | 2024-01-04T03:16:55 | null | NONE | null | ### System Info
```
pip show langchain_community
Name: langchain-community
Version: 0.0.3
```
```
python --version
Python 3.10.12
```
```
pip show langchain_core
Name: langchain-core
Version: 0.1.1
```
```
pip show pydantic
Name: pydantic
Version: 2.5.1
```
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
```
/root/jupyter/pydantic/pydantic/_migration.py:283: UserWarning: `pydantic.error_wrappers:ValidationError` has been moved to `pydantic:ValidationError`.
warnings.warn(f'`{import_path}` has been moved to `{new_location}`.')
Traceback (most recent call last):
File "/root/jupyter/GitHub-Issues/david/langC-rag-lcel.py", line 86, in <module>
chain.invoke("where did harrison work?")
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 1514, in invoke
input = step.invoke(
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2040, in invoke
output = {key: future.result() for key, future in zip(steps, futures)}
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2040, in <dictcomp>
output = {key: future.result() for key, future in zip(steps, futures)}
File "/opt/python-3.10.12/lib/python3.10/concurrent/futures/_base.py", line 458, in result
return self.__get_result()
File "/opt/python-3.10.12/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
raise self._exception
File "/opt/python-3.10.12/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/retrievers.py", line 112, in invoke
return self.get_relevant_documents(
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/retrievers.py", line 211, in get_relevant_documents
raise e
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/retrievers.py", line 204, in get_relevant_documents
result = self._get_relevant_documents(
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_core/vectorstores.py", line 656, in _get_relevant_documents
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_community/vectorstores/docarray/base.py", line 127, in similarity_search
results = self.similarity_search_with_score(query, k=k, **kwargs)
File "/root/jupyter/virtualenv/slack-dev-3.10/lib/python3.10/site-packages/langchain_community/vectorstores/docarray/base.py", line 106, in similarity_search_with_score
query_doc = self.doc_cls(embedding=query_embedding) # type: ignore
File "/root/jupyter/pydantic/pydantic/main.py", line 166, in __init__
__pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
pydantic_core._pydantic_core.ValidationError: 2 validation errors for DocArrayDoc
text
Field required [type=missing, input_value={'embedding': [0.00177018... -0.018160881474614143]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.4/v/missing
metadata
Field required [type=missing, input_value={'embedding': [0.00177018... -0.018160881474614143]}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.4/v/missing
```
### Expected behavior
it should run successfully | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15391 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15391/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15391/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15391/events | https://github.com/langchain-ai/langchain/issues/15391 | 2,062,017,256 | I_kwDOIPDwls565-Lo | 15,391 | [Filter] Unable to filter dates with MongoDB | {
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] | open | false | null | [] | null | 5 | 2024-01-02T07:03:26 | 2024-01-02T10:06:59 | null | NONE | null | ### System Info
Platform: `Mac M1`
Python: `3.8.18`
Lanchain: `0.0.350`
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Install Lanchain
2. Execute the code:
``` python
from datetime import datetime
from datetime import timedelta
from langchain.vectorstores.mongodb_atlas import MongoDBAtlasVectorSearch
from langchain.embeddings import OpenAIEmbeddings
from langchain.chat_models.openai import ChatOpenAI
from langchain.chains import RetrievalQA
db_name = 'YOUR_DB_NAME'
collection_name = 'YOUR_COLLECTION_NAME'
db_connection_string = 'YOUR_STRING'
def perform_retrieval_qa(
query: str,
from_date: str = (datetime.now() - timedelta(days=6)).strftime("%Y-%m-%d"),
to_date: str = datetime.now().strftime("%Y-%m-%d"),
):
print(from_date, to_date)
vector_search = MongoDBAtlasVectorSearch.from_connection_string(
db_connection_string,
f"{db_name}.{collection_name}",
OpenAIEmbeddings(),
index_name="default"
)
llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0)
retrieval_qa_chain = RetrievalQA.from_chain_type(
llm,
retriever=vector_search.as_retriever(
search_kwargs={
'k': 70,
'filter':{
'date': {
'$gte': from_date,
'$lte': to_date
}
}
},
)
)
result = retrieval_qa_chain(
{"query": query},
return_only_outputs=True
)
return result["result"]
```
### Expected behavior
I wish the RAG should be performed with the records from the filtered dates ranges, but it is not happening. The filter is not applied and the RAG is performed with the entire data. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15390 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15390/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15390/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15390/events | https://github.com/langchain-ai/langchain/issues/15390 | 2,062,006,876 | I_kwDOIPDwls5657pc | 15,390 | Issue: milvus collectoin load replica number | {
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] | open | false | null | [] | null | 2 | 2024-01-02T06:49:44 | 2024-01-02T07:01:04 | null | CONTRIBUTOR | null | ### Issue you'd like to raise.
If i set the in-memory replica of milvus to 3 and then run the following code on langchain, the following error occurs.
```python3
vector_db = Milvus.from_documents(
docs,
embeddings,
collection_name=collection,
)
```
> pymilvus.exceptions.MilvusException: <MilvusException: (code=1100, message=failed to load collection: can't change the replica number for loaded collection: expected=3, actual=1: invalid parameter)>
when i look at the langchain code, the `replica_number` is 1 as the default and cannot be handed over as a factor, can you improve this?
```python3
def _load(self) -> None:
"""Load the collection if available."""
from pymilvus import Collection
if isinstance(self.col, Collection) and self._get_index() is not None:
self.col.load()
```
```python3
def load(
self,
partition_names: Optional[list] = None,
replica_number: int = 1,
timeout: Optional[float] = None,
**kwargs,
):
```
### Suggestion:
Please modify the langchain so that the `replica_number` of the milvus load collection can be handed over as an argument | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15389 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15389/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15389/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15389/events | https://github.com/langchain-ai/langchain/pull/15389 | 2,061,998,201 | PR_kwDOIPDwls5jB9PH | 15,389 | Remove unused `_get_python_repl` | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T06:37:30 | 2024-01-02T23:21:00 | 2024-01-02T23:21:00 | CONTRIBUTOR | null | This part of the code can also be safely cleaned up. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15388 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15388/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15388/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15388/events | https://github.com/langchain-ai/langchain/issues/15388 | 2,061,996,098 | I_kwDOIPDwls5655BC | 15,388 | Issue:Issue regarding Memory implementation | {
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] | open | false | null | [] | null | 3 | 2024-01-02T06:34:26 | 2024-01-02T07:59:00 | null | NONE | null | ### Issue you'd like to raise.
`from langchain.chains.question_answering import load_qa_chain
template = """
{Your_Prompt}
CONTEXT:
{context}
QUESTION:
{query}
CHAT HISTORY:
{chat_history}
ANSWER:
"""
prompt = PromptTemplate(input_variables=["chat_history", "query", "context"], template=template)
memory = ConversationBufferMemory(memory_key="chat_history", input_key="query")
chain = load_qa_chain(ChatOpenAI(temperature=0), chain_type="stuff", memory=memory, prompt=prompt)`
is the above code is correct? if correct then please let me know from where i will get chat_history variable?
### Suggestion:
_No response_ | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15387 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15387/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15387/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15387/events | https://github.com/langchain-ai/langchain/pull/15387 | 2,061,902,012 | PR_kwDOIPDwls5jBpIS | 15,387 | Fix: fix Bing Search empty result exception, fix #15384 | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T03:20:21 | 2024-01-03T02:19:34 | 2024-01-02T23:25:01 | CONTRIBUTOR | null | fix https://github.com/langchain-ai/langchain/issues/15384 | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15386 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15386/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15386/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15386/events | https://github.com/langchain-ai/langchain/issues/15386 | 2,061,900,973 | I_kwDOIPDwls565hyt | 15,386 | langchain is not `pyinstaller` friendly due to dependency on external files, e.g. `llm_summarization_checker` | {
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] | closed | false | null | [] | null | 2 | 2024-01-02T03:17:26 | 2024-01-02T04:09:36 | 2024-01-02T04:09:35 | NONE | null | ### System Info
```
langchain==0.0.335
```
```
python 3.11
```
### Who can help?
@hwchase17
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create simple `test.py`:
```python
from langchain.chains.llm_summarization_checker.base import LLMSummarizationCheckerChain
from langchain.llms.fake import FakeListLLM
llm = FakeListLLM(responses=[])
chain = LLMSummarizationCheckerChain.from_llm(llm)
```
2. Install `pyinstaller` via `pip install -U pyinstaller`
3. Run
```
pyinstaller test.py`
```
4. Wait until it's done.
5. Run
```
$ ./dist/test/test
Traceback (most recent call last):
File "test.py", line 1, in <module>
from langchain.chains.llm_summarization_checker.base import LLMSummarizationCheckerChain
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "PyInstaller/loader/pyimod02_importers.py", line 419, in exec_module
File "langchain/chains/__init__.py", line 51, in <module>
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "PyInstaller/loader/pyimod02_importers.py", line 419, in exec_module
File "langchain/chains/llm_summarization_checker/base.py", line 20, in <module>
File "langchain_core/prompts/prompt.py", line 202, in from_file
FileNotFoundError: [Errno 2] No such file or directory: '/home/tiger/junk/tt/dist/test/_internal/langchain/chains/llm_summarization_checker/prompts/create_facts.txt'
[789522] Failed to execute script 'test' due to unhandled exception!
```
### Expected behavior
```
CREATE_ASSERTIONS_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "create_facts.txt")
CHECK_ASSERTIONS_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "check_facts.txt")
REVISED_SUMMARY_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "revise_summary.txt")
ARE_ALL_TRUE_PROMPT = PromptTemplate.from_file(PROMPTS_DIR / "are_all_true_prompt.txt")
```
These lines from https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/llm_summarization_checker/base.py should be changed to something like lazy loading, or just hard code the `.txt` files.
PS - I'm using `llama2` so these prompts do not work anyway. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15385 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15385/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15385/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15385/events | https://github.com/langchain-ai/langchain/issues/15385 | 2,061,894,643 | I_kwDOIPDwls565gPz | 15,385 | DynamoDBChatMessageHistory saved Human Message duplicate | {
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"name": "integration: aws",
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] | closed | false | null | [] | null | 2 | 2024-01-02T03:00:50 | 2024-01-02T08:41:53 | 2024-01-02T08:41:53 | NONE | null | ### System Info
Langchain version: 0.0.351
boto3==1.34.3
Python version: 3.11.7
### Who can help?
I use DynamoDBChatMessageHistory as the conversation_history, seems duplicate Human messages saved to DynamoDB table every time, AI message saved once.
Here is the duplicate messages:
Below is my code:
` def ask_stream(
self,
input_text: str,
conversation_history: ConversationBufferMemory = ConversationBufferMemory(
ai_prefix="Assistant"
),
verbose: bool = False,
**kwargs,
):
"""Processes a stream of input by invoking the engine.
Parameters
----------
conversation_history: ConversationBufferMemory
The conversation history
input_text: str
The input prompt or message.
verbose: boolean
if the langchain shall show the detailed logging
kwargs: dict
Additional keyword arguments to pass to the engine.
For example, you can pass in temperature to control the model's
creativity.
Returns
-------
str
The response generated by the engine.
Raises
------
Any exceptions raised by the engine.
"""
temp_stop = kwargs.get("stop", self.stop)
template = """The following is a friendly conversation between a human and an AI."""
PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
chain = ConversationChain(
llm=self.engine,
memory=conversation_history,
verbose=verbose,
prompt=PROMPT,
)
return chain.predict(input=input_text, stop=temp_stop)
`
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [X] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [X] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Start a new conversation with DynamoDBChatMessageHistory as memory.
### Expected behavior
Each HumanMessage should be saved once in DynamoDB table. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15384 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15384/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15384/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15384/events | https://github.com/langchain-ai/langchain/issues/15384 | 2,061,856,348 | I_kwDOIPDwls565W5c | 15,384 | Bing Search Tool has key value error | {
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] | closed | false | null | [] | null | 1 | 2024-01-02T01:05:50 | 2024-01-02T23:25:02 | 2024-01-02T23:25:02 | NONE | null | ### System Info
Python=3.10
Langchain=0.0.352
langchain-community==0.0.6
I'm using a custom Bing search engine. When I asked something that my Bing search was not able to return results "Webpages" does not exist.
I had to change this: search_results["webPages"]["value"] (line 47) to
if "webPages" in search_results:
return search_results["webPages"]["value"]
else:
return []
### 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
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Create an OpenAI Functions agent with Bing
2. Have a custom bing search
3. Search for something that will not return results from Bing
### Expected behavior
Expect the tool to return "No bing results found" and then a response from an agent | {
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] | closed | false | null | [] | null | 2 | 2024-01-01T22:32:24 | 2024-01-02T03:25:12 | 2024-01-02T03:25:12 | NONE | null | ### Issue with current documentation:
from langchain.vectorstores.neo4j_vector import Neo4jVector
ModuleNotFoundError: No module named 'langchain.vectorstores.neo4j_vector'
### Idea or request for content:
The existing documentation for the Neo4J Vector Index incorrectly indicates the use of "Neo4jVector" from the "langchain.vectorstores" module. However, it appears that there is no implementation of "Neo4jVector" within the Vector stores. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15381 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15381/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15381/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15381/events | https://github.com/langchain-ai/langchain/pull/15381 | 2,061,677,595 | PR_kwDOIPDwls5jA6pN | 15,381 | community:qianfan endpoint support init params & remove useless params definition | {
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] | closed | false | null | [] | null | 1 | 2024-01-01T17:28:48 | 2024-01-02T19:39:06 | 2024-01-01T21:12:31 | CONTRIBUTOR | null | - **Description:**
- support custom kwargs in object initialization. For instantance, QPS differs from multiple object(chat/completion/embedding with diverse models), for which global env is not a good choice for configuration.
- **Issue:** no
- **Dependencies:** no
- **Twitter handle:** no
@baskaryan PTAL
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https://api.github.com/repos/langchain-ai/langchain/issues/15380 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15380/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15380/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15380/events | https://github.com/langchain-ai/langchain/pull/15380 | 2,061,648,914 | PR_kwDOIPDwls5jA0q8 | 15,380 | docs(ollama): Fix Documentation in `CallbackManager`, missing `])` | {
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- **Issue:** N/A
- **Dependencies:** No additional dependencies are required for this change.
- **Twitter handle:** My twitter is @yhzhu99 | {
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To add Async Client support to MongoDB Vector Stores
### Motivation
Currently, Langhain works very well with the PyMongo Client but not with the Async Clients like Motor it throws error, probably I hope it may not be implemented yet?
Reference:
PyMongo Working Docs - https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas
Async Error in Colab - https://colab.research.google.com/drive/1uBfiqoRH6rfiCCXhbxYlNxfc9ILizecl?usp=sharing
### Your contribution
I am not sure. | {
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First, thanks for adding Mistral AI support to LangChain. I started to use it in my application and found that async LLM calls — both for streaming and generation — led to `Unclosed connector` warnings when executed twice or more. In `acompletion_with_retry` the Mistral AI client gets instantiated over and over again without being closed.
To achieve this for both generation and streaming, I've introduced a client manager. The `ClientManager` class inherits from `AsyncCallbackHabdler` and is therefore ready to detect the end of a stream to close the client. And it's a container for client handling in case of not streamed generation.
Besides this feature, I fixed two little bugs:
1. The `stop` parameter is not yet implemented in the API, or am I missing something? I thought it makes sense to keep `_create_message_dicts` and log a warning because support may be added in the future?
2. I completed the call to `on_llm_new_token`, since the library I am currently using relies on it.
What do you think? Looking forward to your feedback.
## Issue
* https://github.com/langchain-ai/langchain/issues/15269
## Dependencies
No dependencies required for this change
## Integration Tests
Executing integration tests on the `master` branch shows multiple `Unclosed connector` warnings. **These are gone when integration tests are executed on this feature branch**. So, IMHO, there is no need to add extra tests here.
✅ `MISTRAL_API_KEY=XYZ make integration_tests` passes
## Misc
**Twitter handle:** @aisneaks
✅ `make format` passes
✅ `make lint` passes
✅ `make test` passes | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15372 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15372/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15372/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15372/events | https://github.com/langchain-ai/langchain/issues/15372 | 2,061,256,601 | I_kwDOIPDwls563EeZ | 15,372 | Playwright Utilities Removed In 0.0.353, Documentation Not Updated | {
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] | open | false | null | [] | null | 2 | 2024-01-01T02:18:08 | 2024-01-01T02:34:24 | null | NONE | null | ### System Info
Langchain Version: 0.0.353
### Who can help?
_No response_
### Information
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Update to 0.0.353
2. Try to import create_async_playwright_browser
3. Get error
### Expected behavior
1. No Error ;D
I can tell that the import was supported as recently as 0.0.350 | {
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] | open | false | null | [] | null | 6 | 2024-01-01T01:46:45 | 2024-01-04T14:47:43 | null | NONE | null | ### System Info
- Python 3.12.1
- MacOS 14.2.1
- langchain-cli 0.0.20 from pip OR langchain-* from git master branch (commit-ish [26f84b7](https://github.com/langchain-ai/langchain/commit/26f84b74d0f7dc4d2211a1a62d47eec36cb1d726)) -- can reproduce with latest code: langchain 0.0.353, langchain-cli 0.0.20, langchain-community 0.0.7, langchain-core 0.1.4
- pandas 2.1.4
- lancedb 0.4.3
- numpy 1.26.2
### 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
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I started with the rag-chroma-private quickstart, then modified it to my needs. I got rid of Chroma due to errors and now use a persistent LanceDB file.
When I try to enter a prompt on the playground, I get a crash (see the bottom of this message).
server.py:
```
from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from langserve import add_routes
from app.chain import chain as rag_private_chain
app = FastAPI()
@app.get("/")
async def redirect_root_to_docs():
return RedirectResponse("/docs")
add_routes(app, rag_private_chain, path="/rag-lancedb-private")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
```
chain.py:
```
# Load
from typing import List
from langchain.chat_models import ChatOllama
from langchain.document_loaders import UnstructuredMarkdownLoader
from langchain.embeddings import OllamaEmbeddings
from langchain.prompts import ChatPromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_core.documents.base import Document
import os, sys
#DB
import lancedb
from langchain.vectorstores.lancedb import LanceDB
#Chroma
#from langchain.vectorstores.chroma import Chroma
#from langchain.vectorstores.utils import filter_complex_metadata
files_directory = "/Users/sean/Downloads/posts"
all_splits = None
my_emb = OllamaEmbeddings(model="llama2:70b-chat")
table = None
db = lancedb.connect("./lance.db")
vectorstore = None
#Generating the vectors only has to be done when the data changes
def do_loader():
#List each file in files_directory and loop through them creating an UnstructuredMarkdownLoader for each file
all_splits = []
for filename in os.listdir(files_directory):
full_path = os.path.join(files_directory, filename)
loader = UnstructuredMarkdownLoader(full_path, mode="elements", strategy="hi_res")
data: List[Document] = loader.load()
all_splits.extend(data)
continue
print(f"Got {len(all_splits)} documents")
table = db.create_table(
"rag",
data=[
{
"vector": my_emb.embed_query("Hello World"),
"text": "Hello World",
"id": "1",
}
],
mode="overwrite",
)
#New
vectorstore = LanceDB.from_documents(all_splits, my_emb, connection=table)
print("Added docs to vector store")
#Not calling do_loader() because I have already populated LanceDB with the vectors.
table = db.open_table("rag")
vectorstore = LanceDB(connection=table, embedding=my_emb)
retriever = vectorstore.as_retriever()
print("Loaded DB")
# Prompt
# Optionally, pull from the Hub
# from langchain import hub
# prompt = hub.pull("rlm/rag-prompt")
# Or, define your own:
template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# LLM
ollama_llm = "llama2:70b-chat"
model = ChatOllama(model=ollama_llm)
print("Created Ollama model")
# RAG chain
chain = (
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
| prompt
| model
| StrOutputParser()
)
# Add typing for input
class Question(BaseModel):
__root__: str
chain = chain.with_types(input_type=Question)
print("Done with chain.py")
```
Error:
```
INFO: Application startup complete.
INFO: 127.0.0.1:64594 - "GET /rag-chroma-private/playground/ HTTP/1.1" 200 OK
INFO: 127.0.0.1:64594 - "GET /rag-chroma-private/playground/assets/index-52e8ab2f.css HTTP/1.1" 200 OK
INFO: 127.0.0.1:64595 - "GET /rag-chroma-private/playground/assets/index-6a0f524c.js HTTP/1.1" 200 OK
INFO: 127.0.0.1:64595 - "GET /rag-chroma-private/playground/favicon.ico HTTP/1.1" 200 OK
INFO: 127.0.0.1:64594 - "POST /rag-chroma-private/stream_log HTTP/1.1" 200 OK
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/sse_starlette/sse.py", line 269, in __call__
await wrap(partial(self.listen_for_disconnect, receive))
File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/sse_starlette/sse.py", line 258, in wrap
await func()
File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/sse_starlette/sse.py", line 215, in listen_for_disconnect
message = await receive()
^^^^^^^^^^^^^^^
File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py", line 538, in receive
await self.message_event.wait()
File "/opt/homebrew/Cellar/python@3.12/3.12.1/Frameworks/Python.framework/Versions/3.12/lib/python3.12/asyncio/locks.py", line 212, in wait
await fut
asyncio.exceptions.CancelledError: Cancelled by cancel scope 15bc21f40
During handling of the above exception, another exception occurred:
+ Exception Group Traceback (most recent call last):
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/uvicorn/protocols/http/h11_impl.py", line 408, in run_asgi
| result = await app( # type: ignore[func-returns-value]
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/uvicorn/middleware/proxy_headers.py", line 84, in __call__
| return await self.app(scope, receive, send)
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/fastapi/applications.py", line 1054, in __call__
| await super().__call__(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/applications.py", line 116, in __call__
| await self.middleware_stack(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 186, in __call__
| raise exc
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/middleware/errors.py", line 164, in __call__
| await self.app(scope, receive, _send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/middleware/exceptions.py", line 62, in __call__
| await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
| raise exc
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
| await app(scope, receive, sender)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/routing.py", line 754, in __call__
| await self.middleware_stack(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/routing.py", line 774, in app
| await route.handle(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/routing.py", line 296, in handle
| await self.app(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/routing.py", line 75, in app
| await wrap_app_handling_exceptions(app, request)(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
| raise exc
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
| await app(scope, receive, sender)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/starlette/routing.py", line 73, in app
| await response(scope, receive, send)
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/sse_starlette/sse.py", line 255, in __call__
| async with anyio.create_task_group() as task_group:
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 678, in __aexit__
| raise BaseExceptionGroup(
| ExceptionGroup: unhandled errors in a TaskGroup (1 sub-exception)
+-+---------------- 1 ----------------
| Traceback (most recent call last):
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/langserve/serialization.py", line 90, in default
| return super().default(obj)
| ^^^^^^^
| RuntimeError: super(): __class__ cell not found
|
| The above exception was the direct cause of the following exception:
|
| Traceback (most recent call last):
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/sse_starlette/sse.py", line 258, in wrap
| await func()
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/sse_starlette/sse.py", line 245, in stream_response
| async for data in self.body_iterator:
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/langserve/api_handler.py", line 1049, in _stream_log
| "data": self._serializer.dumps(data).decode("utf-8"),
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| File "/Users/sean/dev/cc/cc/ccvenv/lib/python3.12/site-packages/langserve/serialization.py", line 168, in dumps
| return orjson.dumps(obj, default=default)
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
| TypeError: Type is not JSON serializable: numpy.ndarray
+------------------------------------
```
### Expected behavior
It's not clear to me which part of the stack is failing, but the expected behavior is that the LLM should generate some output rather than crashing. | {
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] | closed | false | null | [] | null | 2 | 2024-01-01T01:32:47 | 2024-01-01T02:26:06 | 2024-01-01T02:26:06 | NONE | null | ### System Info
MacOS 14.0, Jupiter with Python 3.11.6
(base) ➜ llm-env pip show langchain
Name: langchain
Version: 0.0.353
Summary: Building applications with LLMs through composability
Home-page: https://github.com/langchain-ai/langchain
Author:
Author-email:
License: MIT
Location: /lib/python3.11/site-packages
Requires: aiohttp, dataclasses-json, jsonpatch, langchain-community, langchain-core, langsmith, numpy, pydantic, PyYAML, requests, SQLAlchemy, tenacity
### Who can help?
_No response_
### Information
- [x] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
when try to execute,
from langchain.agents.agent_toolkits import create_python_agent
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentType
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
Jupiter gave an error:
ValueError: 'lib/python3.11/site-packages/langchain/agents/agent_toolkits' is not in the subpath of 'lib/python3.11/site-packages/langchain_core' OR one path is relative and the other is absolute.
### Expected behavior
Import agent related thing correctly. | {
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] | open | false | null | [] | null | 3 | 2024-01-01T00:04:18 | 2024-01-17T19:01:24 | null | COLLABORATOR | null | The `langchain.prompts.example_selector` [still holds several artifacts](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.prompts) that belongs to `community`. If they moved to `langchain_community.example_selectors`, the `langchain.prompts` namespace would be effectively removed which is great.
- moved a class and afunction to `langchain_community`
Note:
- Previously, the `langchain.prompts.example_selector` artifacts were moved into the `langchain_core.exampe_selectors`. See the flattened namespace (`.prompts` was removed)!
Similar flattening was implemented for the `langchain_core` as the `langchain_core.exampe_selectors`. | {
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] | open | false | null | [] | null | 3 | 2023-12-31T23:26:43 | 2024-01-17T19:02:55 | null | COLLABORATOR | null | The `langchain` [still holds several artifacts](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.tools) that belongs to `core`. If they moved then `langchain.tools` would be split between `core` and `community` (no `langchain` artifacts), which is nice. It effectively removes the `langchain.tools` namespace.
- moved a class and functions to `core` | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15366 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15366/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15366/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15366/events | https://github.com/langchain-ai/langchain/pull/15366 | 2,061,165,478 | PR_kwDOIPDwls5i_L0k | 15,366 | docs: revamp redirects | {
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] | closed | false | null | [] | null | 1 | 2023-12-31T20:48:57 | 2023-12-31T21:26:50 | 2023-12-31T21:26:49 | COLLABORATOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
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The ollama integration assumes that all models are served on "localhost:11434", if the ollama service is hosted on a different machine, the integration will fail.
Can we add an environment variable that if present overrides this url, so the correct url for the ollama server can be set.
### Motivation
In My setup ollama sits on a separate machine that is resourced for serving LLMs.
### Your contribution
I'm afraid I don't have any knowledge of python, go, cpp and rust only. | {
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] | closed | false | null | [] | null | 1 | 2023-12-31T19:46:50 | 2024-01-02T19:39:55 | 2024-01-01T21:38:14 | CONTRIBUTOR | null | - **Description:** Fix a few spelling and grammar issues
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** @donovancmuller
<!-- Thank you for contributing to LangChain!
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added self-query template for Vectara
Also added prompt_name parameter to summarization
CC @efriis
**Twitter handle:** @ofermend | {
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langchain 0.0.353
pygpt4all 1.1.0
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
from langchain.llms import GPT4ALL, LlamaCpp
### Expected behavior
ImportError: cannot import name 'GPT4ALL' from 'langchain.llms' (/home/rkuo/.local/lib/python3.10/site-packages/langchain/llms/__init__.py) | {
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https://github.com/langchain-ai/langchain/issues/15360 | {
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Python 3.9
LangChain 0.0.339
### 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
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I created new instance of SQLDatabase and executed SQL queries using the 'run' method.
The problem is that when I execute a simple SELECT query the result returns without the column names, only the values, and therefore I can't tell which value relates to which column.
I saw in the implementation the following code in the 'run' method:
```
result = self._execute(command, fetch)
# Convert columns values to string to avoid issues with sqlalchemy
# truncating text
res = [
tuple(truncate_word(c, length=self._max_string_length) for c in r.values())
for r in result
]
```
and I'm wondering if this data manipulation is done by intention or maybe it's a bug.
### Expected behavior
For example:
executes the following SQL: select id, count(*) as num_count from some_table
expected result: [{'id': 383, 'num_count': 10}]
actual result: [(383, 10)] | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15359 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15359/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15359/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15359/events | https://github.com/langchain-ai/langchain/issues/15359 | 2,061,090,976 | I_kwDOIPDwls562cCg | 15,359 | AttributeError: 'VectorStoreRetriever' object has no attribute 'args_schema' | {
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] | open | false | null | [] | null | 2 | 2023-12-31T15:17:25 | 2024-01-03T06:53:39 | null | NONE | null | ### System Info
- LangChain: 0.0.353
- System: Ubuntu 22.04
- Python: 3.10.12
### Information
I run the code in the quickstart part of the [document](https://python.langchain.com/docs/get_started/quickstart#agent), code:
```python
from langchain.chat_models import ChatOpenAI
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/openai-functions-agent")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
```
However, the Python interpreter told me:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/tmp/ipykernel_665358/3241410387.py in <module>
7 prompt = hub.pull("hwchase17/openai-functions-agent")
8 llm = ChatOpenAI(openai_api_key=openai_api_key, model="gpt-3.5-turbo", temperature=0)
----> 9 agent = create_openai_functions_agent(llm=llm, tools=tools, prompt=prompt)
10 agent_executor = AgentExecutor(agent, tools, verbose=True)
[~/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py](https://vscode-remote+ssh-002dremote-002b158-002e132-002e9-002e210.vscode-resource.vscode-cdn.net/home/iot/Documents/langchain/~/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py) in create_openai_functions_agent(llm, tools, prompt)
285 )
286 llm_with_tools = llm.bind(
--> 287 functions=[format_tool_to_openai_function(t) for t in tools]
288 )
289 agent = (
[~/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py](https://vscode-remote+ssh-002dremote-002b158-002e132-002e9-002e210.vscode-resource.vscode-cdn.net/home/iot/Documents/langchain/~/.local/lib/python3.10/site-packages/langchain/agents/openai_functions_agent/base.py) in <listcomp>(.0)
285 )
286 llm_with_tools = llm.bind(
--> 287 functions=[format_tool_to_openai_function(t) for t in tools]
288 )
289 agent = (
[~/.local/lib/python3.10/site-packages/langchain_community/tools/convert_to_openai.py](https://vscode-remote+ssh-002dremote-002b158-002e132-002e9-002e210.vscode-resource.vscode-cdn.net/home/iot/Documents/langchain/~/.local/lib/python3.10/site-packages/langchain_community/tools/convert_to_openai.py) in format_tool_to_openai_function(tool)
10 def format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
11 """Format tool into the OpenAI function API."""
---> 12 if tool.args_schema:
13 return convert_pydantic_to_openai_function(
14 tool.args_schema, name=tool.name, description=tool.description
AttributeError: 'VectorStoreRetriever' object has no attribute 'args_schema'
```
It seems that some packages have version incompatibility.
- [X] The official example notebooks/scripts
- [ ] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [X] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
1. Directly run the following code:
```
from langchain.chat_models import ChatOpenAI
from langchain import hub
from langchain.agents import create_openai_functions_agent
from langchain.agents import AgentExecutor
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/openai-functions-agent")
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
```
### Expected behavior
It should run successfully without any error report. | {
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https://api.github.com/repos/langchain-ai/langchain/issues/15358 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15358/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15358/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15358/events | https://github.com/langchain-ai/langchain/pull/15358 | 2,061,084,185 | PR_kwDOIPDwls5i-80r | 15,358 | Langchain_community: Small Fix when loading facebook messages | {
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- **Issue:** #15356
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https://api.github.com/repos/langchain-ai/langchain/issues/15357 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15357/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15357/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15357/events | https://github.com/langchain-ai/langchain/pull/15357 | 2,061,030,736 | PR_kwDOIPDwls5i-zBO | 15,357 | Batch update of alt text and title attributes for images in md/mdx files across repo | {
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] | null | 4 | 2023-12-31T11:33:26 | 2024-01-12T22:37:49 | 2024-01-12T22:37:49 | CONTRIBUTOR | null | **Description:** Batch update of alt text and title attributes for images in `md` & `mdx` files across the repo using [alttexter](https://github.com/jonathanalgar/alttexter)/[alttexter-ghclient](https://github.com/jonathanalgar/alttexter-ghclient) (built using LangChain/LangSmith).
**Limitation:** cannot update `ipynb` files because of [this issue](https://github.com/langchain-ai/langchain/pull/15357#issuecomment-1885037250). Can revisit when Docusaurus is bumped to v3.
I checked all the generated alt texts and titles and didn't find any technical inaccuracies. That's not to say they're _perfect_, but a lot better than what's there currently.
[Deployed](https://langchain-819yf1tbk-langchain.vercel.app/docs/modules/model_io/) image example:
You can see LangSmith traces for all the calls out to the LLM in the PRs merged into this one:
* https://github.com/jonathanalgar/langchain/pull/6
* https://github.com/jonathanalgar/langchain/pull/4
* https://github.com/jonathanalgar/langchain/pull/3
I didn't add the following files to the PR as the images already have OK alt texts:
* https://github.com/langchain-ai/langchain/blob/27dca2d92fbd9c956a1eac7cf885a0ed060ca9ff/docs/docs/integrations/providers/argilla.mdx#L3
* https://github.com/langchain-ai/langchain/blob/27dca2d92fbd9c956a1eac7cf885a0ed060ca9ff/docs/docs/integrations/providers/apify.mdx#L11 | {
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] | closed | false | null | [] | null | 3 | 2023-12-31T09:31:07 | 2024-01-02T14:36:02 | 2024-01-02T14:36:01 | NONE | null | ### System Info
langchain 0.0.353
Python 3.10.13 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:24:38) [MSC v.1916 64 bit (AMD64)]
Windows 11
### 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
```python
from langchain.chat_models.openai import ChatOpenAI
from langchain_community.chat_loaders.facebook_messenger import FolderFacebookMessengerChatLoader, SingleFileFacebookMessengerChatLoader
from pathlib import Path
import os
chat_file = Path("data/my-fb-folder/messages/inbox/message-dir/message_1.json")
loader = SingleFileFacebookMessengerChatLoader(chat_file)
loader.load()
```
Stacktrace:
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In[11], [line 8](vscode-notebook-cell:?execution_count=11&line=8)
[6](vscode-notebook-cell:?execution_count=11&line=6) chat_file = Path("data/my-fb-folder/your_activity_across_facebook/messages/inbox/message-dir/message_1.json")
[7](vscode-notebook-cell:?execution_count=11&line=7) loader = SingleFileFacebookMessengerChatLoader(chat_file)
----> [8](vscode-notebook-cell:?execution_count=11&line=8) loader.load()
File [c:\Users\th4tkh13m\miniconda3\envs\rag\lib\site-packages\langchain_community\chat_loaders\base.py:16](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/base.py:16), in BaseChatLoader.load(self)
[14](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/base.py:14) def load(self) -> List[ChatSession]:
[15](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/base.py:15) """Eagerly load the chat sessions into memory."""
---> [16](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/base.py:16) return list(self.lazy_load())
File [c:\Users\th4tkh13m\miniconda3\envs\rag\lib\site-packages\langchain_community\chat_loaders\facebook_messenger.py:43](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:43), in SingleFileFacebookMessengerChatLoader.lazy_load(self)
[39](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:39) messages = []
[40](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:40) for m in sorted_data:
[41](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:41) messages.append(
[42](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:42) HumanMessage(
---> [43](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:43) content=m["content"], additional_kwargs={"sender": m["sender_name"]}
[44](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:44) )
[45](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:45) )
[46](file:///C:/Users/th4tkh13m/miniconda3/envs/rag/lib/site-packages/langchain_community/chat_loaders/facebook_messenger.py:46) yield ChatSession(messages=messages)
KeyError: 'content'
```
### Expected behavior
The chat message should be loaded normally. | {
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] | open | false | null | [] | null | 1 | 2023-12-31T08:43:04 | 2023-12-31T08:48:30 | null | NONE | null | ### System Info
azure-search-documents==11.4.0b8
langchain==0.0.352
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I have my own search index with no `metadata` field.
#### Code
```python
from langchain.vectorstores.azuresearch import AzureSearch
vector_store: AzureSearch = AzureSearch(
azure_search_endpoint=vector_store_address,
azure_search_key=vector_store_password,
index_name=index_name,
embedding_function=embeddings.embed_query,
semantic_configuration_name="default"
)
query = "How many employees does Contoso Electronics have?"
docs = vector_store.semantic_hybrid_search(
query=query,
search_type="semantic_hybrid",
)
print(docs[0])
```
#### Stack trace
```
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File \lib\site-packages\langchain_community\vectorstores\azuresearch.py:656, in <listcomp>(.0)
622 semantic_answers_dict[semantic_answer.key] = {
623 "text": semantic_answer.text,
624 "highlights": semantic_answer.highlights,
625 }
626 # Convert results to Document objects
627 docs = [
628 (
629 Document(
630 page_content=result.pop(FIELDS_CONTENT),
631 metadata={
632 **(
633 {FIELDS_ID: result.pop(FIELDS_ID)}
634 if FIELDS_ID in result
635 else {}
636 ),
637 **(
638 json.loads(result[FIELDS_METADATA])
639 if FIELDS_METADATA in result
640 else {
641 k: v
642 for k, v in result.items()
643 if k != FIELDS_CONTENT_VECTOR
644 }
645 ),
646 **{
647 "captions": {
648 "text": result.get("@search.captions", [{}])[0].text,
649 "highlights": result.get("@search.captions", [{}])[
650 0
651 ].highlights,
652 }
653 if result.get("@search.captions")
654 else {},
655 "answers": semantic_answers_dict.get(
--> 656 json.loads(result["metadata"]).get("key"), ""
657 ),
658 },
659 },
660 ),
661 float(result["@search.score"]),
662 float(result["@search.reranker_score"]),
663 )
664 for result in results
665 ]
666 return docs
KeyError: 'metadata'
```
### Expected behavior
I get search results from Azure AI Search.
This error is caused by the hardcoding of `metadata` field name, such as `result["metadata"]` in line 656 of `langchain\libs\community\langchain_community\vectorstores\azuresearch.py`. Therefore, performing a search on an Azure AI Search index that does not have this field will fail. | {
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] | closed | false | null | [] | null | 1 | 2023-12-31T05:13:29 | 2023-12-31T05:13:46 | 2023-12-31T05:13:44 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
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Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
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] | closed | false | null | [] | null | 1 | 2023-12-31T04:23:42 | 2023-12-31T04:39:11 | 2023-12-31T04:35:07 | CONTRIBUTOR | null | <!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
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- **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on network access,
2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
-->
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- Adding commands to install `tiktoken` and `langchainhub`
- Adds a comma between 2 parameters for one of the methods
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] | closed | false | null | [] | null | 1 | 2023-12-31T00:49:41 | 2024-01-02T18:13:35 | 2024-01-02T18:13:34 | COLLABORATOR | null | <!-- Thank you for contributing to LangChain!
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https://api.github.com/repos/langchain-ai/langchain/issues/15350 | https://api.github.com/repos/langchain-ai/langchain | https://api.github.com/repos/langchain-ai/langchain/issues/15350/labels{/name} | https://api.github.com/repos/langchain-ai/langchain/issues/15350/comments | https://api.github.com/repos/langchain-ai/langchain/issues/15350/events | https://github.com/langchain-ai/langchain/issues/15350 | 2,060,892,236 | I_kwDOIPDwls561rhM | 15,350 | Issue: output templates in langchain | {
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] | open | false | null | [] | null | 1 | 2023-12-31T00:18:00 | 2023-12-31T00:24:06 | null | NONE | null | ### Issue you'd like to raise.
How can I make output templates in langchain? That is, for example, I throw a request for AI to write a joke, but with strict adherence to the template [set-up, punchline] and therefore get as a result:
```
Set-up: ...
Punchline: ...
```
and nothing more
### Suggestion:
_No response_ | {
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] | closed | false | null | [] | null | 3 | 2023-12-30T18:38:19 | 2023-12-31T12:17:59 | 2023-12-31T12:17:59 | NONE | null | ### System Info
Langchain 0.0.353
Python 3.10.12
System Ubuntu 22.04
### Who can help?
_No response_
### Information
- [ ] The official example notebooks/scripts
- [X] My own modified scripts
### Related Components
- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async
### Reproduction
I am trying to modify and run the example on [this page](https://python.langchain.com/docs/use_cases/question_answering/). I am modifying it slightly to use a different embedding tool from the Ollama model.
The last line, which should create the vector store, in the below snippet crashes.
```
import bs4
from langchain import hub
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import WebBaseLoader
from langchain.embeddings import OllamaEmbeddings
from langchain.schema import StrOutputParser
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough
embeddings_open = OllamaEmbeddings(model="mistral")
loader = WebBaseLoader(
web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("post-content", "post-title", "post-header")
)
),
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings_open)
```
### Expected behavior
I would expect the code to work, unless I'm missing something important. Instead, I get this error.
Any clues are most appreciated. I'm sure it is something simple I overlooked.
```
>>> vectorstore = Chroma.from_documents(documents=splits, embedding = embeddings_open)
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/urllib3/connection.py", line 169, in _new_conn
conn = connection.create_connection(
File "/usr/lib/python3/dist-packages/urllib3/util/connection.py", line 96, in create_connection
raise err
File "/usr/lib/python3/dist-packages/urllib3/util/connection.py", line 86, in create_connection
sock.connect(sa)
ConnectionRefusedError: [Errno 111] Connection refused
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/requests/adapters.py", line 486, in send
resp = conn.urlopen(
File "/usr/lib/python3/dist-packages/urllib3/connectionpool.py", line 756, in urlopen
retries = retries.increment(
File "/usr/lib/python3/dist-packages/urllib3/util/retry.py", line 574, in increment
raise MaxRetryError(_pool, url, error or ResponseError(cause))
urllib3.exceptions.MaxRetryError: HTTPConnectionPool(host='localhost', port=11434): Max retries exceeded with url: /api/embeddings (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7f8d11256bc0>: Failed to establish a new connection: [Errno 111] Connection refused'))
``` | {
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